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New features and improvements introduced from January to March 2025
March 19th, 2025
Improvement Builder
Editor users can now manage the presence of a layer in the map layer list directly from the Legend tab in Builder. Previously, it was only possible to show or hide a layer’s legend. With this update, you now have full control over whether a layer itself should appear in the map layer list — what end-users see and interact with during map exploration.
March 19th, 2025
New CARTO for Developers
As organizations expand their usage of CARTO and break the GIS data silo using cloud-native maps and workflows, it becomes important to have the right tools to manage all resources at scale. This is why, starting today, all users in CARTO have access to a new set of API endpoints where they can programmatically list and delete their maps, workflows, and connections.
Additionally, to empower Superadmins on their journey to enable CARTO for large organizations, we're exposing the following functionality via the new APIs:
List all the maps, workflows, and connections in a CARTO organization
Bulk delete of multiple assets with a single API request
Transfer the ownership of an asset (map, workflow, or connection) to another user
Ready to scale up? Head over to our API reference to get started.
March 13th, 2025
New Workspace
Users can now connect to their Databricks account using OAuth authentication, with both Machine-to-Machine (M2M) and User-to-Machine (U2M) authentication flows supported! This adds an extra layer of security for Databricks users since OAuth tokens are automatically refreshed by default and do not require the direct management of the access token. For these reasons, Databricks is strongly recommending its users to choose OAuth over Personal Access Tokens.
Want to learn more? head over to our section on Databricks connections.
February 27th, 2025
New Builder
Raster visualization is now available in Builder, marking a major milestone in CARTO’s end-to-end support for raster data. With this release, you can seamlessly import, analyze, and visualize raster datasets stored in Google BigQuery and Snowflake—all within CARTO.
This new capability unlocks powerful use cases, allowing you to explore and analyze data at scale, seamlessly within your cloud environment, without additional data movement. Interesting in learning more? Check our documentation.
February 20th, 2025
New Builder
We’re excited to announce the Public Preview of CARTO AI Agents, designed to make interacting with your maps in Builder more intuitive and dynamic. With AI Agents, users can seamlessly zoom to specific regions based on conversational input, explore map details, and apply filters using widgets—all through a natural language interface.
✨ Stay tuned—this is just the beginning. We’re already working on making AI Agents faster, smarter, and more powerful to elevate your mapping experience even further.
February 18th, 2025
New Workflows
We’re introducing Location Data Services (LDS) support and new data enrichment components in CARTO for Databricks, enabling more seamless geospatial analysis across different user roles and workflows.
Location Data Services (LDS) Support: Now available in both the Analytics Toolbox for Databricks and as Workflows components. Users can perform geocoding, routing, and isoline calculations via CARTO’s standard providers. The Analytics Toolbox enables direct use within Databricks notebooks and SQL workflows, while CARTO Workflows provides a low-code interface, integrating LDS into broader spatial analysis pipelines. LDS usage is subject to CARTO licensing and quotas, but users can also bring their own provider credentials, just as with other data warehouses.
Data Enrichment Components: These new Workflows components simplify use cases like demographic enrichment, POI data integration, and trade area analysis. Users can enhance datasets with information from CARTO’s Data Observatory or their own geospatial sources, whether structured as spatial indexes, points, or polygons. By embedding enrichment within CARTO Workflows, users can more easily integrate this step into their existing analysis.
These updates further reduce complexity for Databricks users working with spatial data. Data scientists can leverage LDS functions directly within their Databricks environment, while Workflows opens up more advanced spatial analysis to less technical users. By bringing LDS and enrichment into CARTO Workflows, we make it easier to build complete geospatial pipelines without writing custom code.
February 6th, 2025
New Integrations
The new CARTO QGIS Plugin allows you to access, visualize, and edit spatial data from leading cloud data warehouses directly within QGIS. With this plugin, you can seamlessly check out data from Google BigQuery, Snowflake, Databricks, AWS Redshift, and PostgreSQL, edit it within QGIS, and commit changes back to your data warehouse—all powered by the CARTO platform.
Simply connect your cloud data warehouse to CARTO, install the QGIS plugin, and gain full control over your geospatial data in a familiar GIS environment. This enables smooth workflows for spatial data management, enrichment, and analysis while ensuring your data remains centralized and up to date in your cloud ecosystem.
February 3rd, 2025
New Workspace
Snowflake users can now connect to their Data Warehouse using Key-pair authentication! This is a much more secure alternative to basic username/password authentication as it is highly resistant to brute-force attacks, eliminates password management complexities, and can be easily used as the authentication mechanism for scripts and applications.
We've also added support for Key-pair rotation, enabling users to update the private key of Key-pair connections they own. For more information, see our section on Key-pair authentication for Snowflake connections.
January 30th, 2025
New Builder
Are you working with datasets where multiple rows share the same geometry but have varying attributes, such as administrative boundaries, roads, or infrastructure locations?
The new aggregate by geometry functionality allows you to aggregate those features in your layer visualization and interactions, improving performance while keeping access to detailed insights.
With this update, you can:
Aggregate geometries in your layer to ensure optimal performance.
Aggregate styling and interaction attributes to retrieve relevant information linked to your aggregated feature.
Maintain widget functionality over the original source, enabling drill-down operations for deeper analysis.
January 30th, 2025
New Workflows
With this new release, users and partners can now extend the capabilities of our low-code analytics tool CARTO Workflows by creating, integrating and distributing custom components tailored to their specific spatial analytics needs.
To start creating your own Workflows Extension Packages we have published this public GitHub template. Kick off your own repository out the template and start developing extensions for BigQuery and Snowflake connections.
Additionally, we have published a set of extensions readily available to be installed from the Workflows UI. The initial release boasts a curated collection of extensions, including:
BigQuery ML: Integrate machine learning workflows with your geospatial data using BigQuery ML directly within Workflows.
Google Earth Engine: Unleash the power of Google Earth Engine for advanced spatial analysis tasks.
Google Environment APIs: Bring the power of Google Environment APIs (Solar, Air Quality, Pollen) into your geospatial analytics workflows.
Telco Signal Propagation Models: Analyze telecommunication signals with path profiles, propagation modeling, and obstacle identification.
Head over to the CARTO Workflows documentation to learn more about Extension Packages and explore the initial release offerings.
January 17th, 2025
New Workspace
We've introduced the ability to share maps with individual users! Previously, maps could only be shared with the entire organization, specific user groups, or made publicly accessible via a link.
With this new feature, Editors now have more granular control over map access permissions. Users can select exactly which individuals should have access to a map (and they can revoke it at any time), making it easier to collaborate on specific projects while maintaining security. For more information, see our section on publishing and sharing maps.
January 10th, 2025
New Workspace
We’re excited to announce that CARTO now supports connecting to Google BigQuery via Workload Identity Federation! This new capability enables secure, seamless authentication without requiring service account keys, making it easier to manage access and improving security for your cloud-native maps, workflows and applications.
With Workload Identity Federation, you can set up a trust relationship between CARTO and your Google Cloud projects for a smooth integration — In other words, you will be managing permissions to each of your CARTO users directly in Google Cloud, using IAM rules.
Another benefit of this method is that it provides a framework to effortlessly scale and distribute granular permissions across large-scale teams using CARTO and BigQuery. To get started:
Administrators will need to set up an integration to configure Workload Identity Federation in CARTO.
Once the integration is set up, all users will be able to use Workload Identity Federation when connecting CARTO and BigQuery.
January 7th, 2025
Improvement CARTO for Developers
A few months ago we introduced our framework-agnostic widgets, a new system for developers to add scalable and highly-performant charts and other data components to their CARTO + deck.gl application, with support for vector-based data sources: points, lines and polygons.
Today, we're extremely happy to announce that developers can now build completely custom widgets using spatial index sources as well. These sources aggregate the data in a spatial index system, such as H3 or Quadbin, for increased performance and scalability. The main benefits of the new framework-agnostic widgets apply to spatial index-based widgets as well:
Build anything using H3 and Quadbin sources: from scorecards to bar charts, tables, time series, and everything in between.
Bring your own UI: Use your favorite charting library or custom HTML components.
Easily sync your widgets with the deck.gl map.
Seamlessly use widgets to filter the map and other widgets, fully leveraging your cloud data warehouse computing power.
Built using JS and Typescript only, they are fully compatible with the framework of your choice (Angular, React, Vue...), adding minimal dependencies.
Ready to learn more? Get started by reading the technical reference or by exploring the examples.
New features and improvements introduced from October to December 2023
December 20th, 2023
Improvement Builder
We've upgraded the export functionality in Builder maps, shifting the data export process to work in server-side mode for an enhanced efficiency and data integrity. This improvement ensures a more reliable data retrieval experience.
Additionally, when exporting data as CSV, it now includes the geometry column in WKT (Well-Known Text) format, if applicable. This enhancement simplifies data handling and boosts compatibility with various geospatial tools, making integrations smoother.
Looking to leverage this enhanced functionality for RDS for PostgreSQL data sources? Don't forget to set up the necessary S3 bucket integration to enable the export feature. For more details and guidance, check out our documentation.
December 18th, 2023
New Workflows
We have added a new component to Workflows that leverages BigQuery ML Generate Text capabilities to allow embedding Generative AI functionalities into your geospatial analytical pipelines.
It can be used to help analyze the result of an enrichment; to generate labels or categories based on variables on your table; or could also generate new content for each row on your data, using different variables to compose a prompt that will be evaluated on each row.
With this new addition, Generative AI capabilities are handy and readily available from Workflows.
November 28th, 2023
New Workflows
We have just released a new component in Workflows that allows exporting the result from any node in a workflow to a storage bucket.
The node's data will be exported as a series of files, which URLs will be stored in a table. Just inspect the Data tab in the results panel to access the links to each file.
This component is currently available for all CARTO Data Warehouse and BigQuery connections.
November 27th, 2023
Improvement Workflows
We have added a few improvements to the results panel in Workflows that are focused on better usability and data exploration capabilities:
Renovated, sleeker design;
See when the workflow was executed for the last time;
Easily find the number of columns and rows of a result;
Explore the complete result with pagination for optimal performance;
Copy the content of a page to the clipboard, ready to be pasted into a spreadsheet;
Analyze statistics of each column:
Frequency of the Top 20 categories for string, date and timestamp columns;
Maximum and minimum values, average and sum for numeric columns.
SQL control code is hidden to facilitate readability.
Find all the documentation about these improvements here.
November 22nd, 2023
New Workspace
You can now set up an OAuth integration to connect CARTO and Snowflake. This allows users to follow their usual Snowflake login flow (Snowflake OAuth) to set up their connections in CARTO, which has security benefits and is a more familiar process for all Snowflake users.
If you have an external identity provider integrated in Snowflake such as Azure Active Directory or Okta, we also support External OAuth to achieve the same process.
Read more about Configuring OAuth connections to Snowflake.
November 16th, 2023
New Builder
We're excited to announce the latest feature in Builder - a distance measure tool that will allow users to measure distances between two points on their maps.
This new functionality is ideal for a diverse range of use cases, from planning tasks to gaining a deeper understanding of spatial relationships between various map elements.
November 6th, 2023
New Builder
In Builder, you now have the capability to style your qualitative data using hex color codes pulled directly from your table or SQL query sources. If you're curious about generating these hex color codes, we've prepared a tutorial to assist you, detailing the steps using either Workflows or SQL. What's especially exciting? The range of possibilities this opens up. Whether you're aligning with your company's branding, looking to automatically style a high number of categories, or exploring diverse color schemes, the choice is all yours.
October 25th, 2023
New Workflows
We have just published a new collection of workflows examples, designed with a hands-on approach to empower users and ease the learning curve for using CARTO Workflows.
It showcases a wide range of scenarios, from simple building blocks for your geospatial analysis to more complex, industry-specific workflows tailored to suit specific use cases.
Take a look at our catalog of workflows examples here!
October 25th, 2023
New Workflows
We have added a new mechanism to import a workflow into your account. Just download an example from the gallery, drag and drop into your CARTO Workspace (or browse a file from your computer) and the workflow will be automatically re-created in your account.
And also we have just added a new way to import data into a workflow. Either from a local file in your computer, or from a URL, this new feature facilitates the task of incorporating data into your analytical pipelines.
October 18th, 2023
New Builder
The new Pie Widget is designed to simplify the visualization of complex categorical data in Builder, making it more user-friendly and insightful.
Thanks to this new feature you can quickly and easily analyze data proportions and category weights, allowing for better understanding of each data category within your dataset. This enhancement empowers users to make more informed decisions by providing a clearer view of their data.
Welcome to the CARTO Documentation Center! All of the resources you need to unlock the power of the platform.
CARTO is the only cloud-first spatial platform built for accelerated, modern GIS. It runs natively on top of your cloud data warehouse platform (e.g. Google BigQuery, Snowflake, AWS Redshift, etc.), providing easy access to highly scalable spatial analysis and visualization capabilities in the cloud — be it for analytics, app development, data engineering, and more.
CARTO unlocks the power of spatial analysis in the cloud, extending the visualization, analysis and development capabilities of the leading cloud data warehouse platforms, such as Google BigQuery, Snowflake and Amazon Redshift.
Find out how to get the most out of our Location Intelligence platform with our product documentation:
New features and improvements introduced from January to March 2024
March 31st, 2024
New Builder
This new feature simplifies the map-making process by letting Editor users switch seamlessly between editing and previewing. With Preview mode, these users can easily see how the map will look like to viewers, allowing them to review and refine it before sharing. This smooth workflow ensures that maps are well-presented and meet the highest standards of clarity and effectiveness.
Additionally we've enhanced our map-sharing functionality to deliver a smoother and more intuitive experience. This update focuses on streamlining the process of sharing maps with others, ensuring a more seamless interaction. Dive into the details of these improvements in our documentation.
March 27th, 2024
New CARTO for Developers
A new major version of deck.gl is out. deck.gl is the open-source visualization library that powers all CARTO visualizations, and one of the main components of CARTO for Developers.
For a complete changelog, visit the official deck.gl what's new.
To address breaking changes, read the official deck.gl upgrade guide. Changes in the CARTO module are also addressed there.
We have also published a complete set of new examples using CARTO + deck.gl.
We're very happy to see CARTO joining efforts with many other contributors from the vis.gl and OpenJS Foundation communities. Read more about this release in the CARTO blog.
March 21st, 2024
New Workflows
With this new capability, analytical pipelines created with Workflows can be scheduled so they are executed on a specific period:
Hours: The workflow will be executed every X hours, at o'clock times.
Days: The workflow will be executed every day at a specific time.
Weeks: The workflow will be executed weekly, on a specific day, at a specific time.
Months: The workflow will be executed monthly, on a specific day, at a specific time.
Custom: Use a custom expression to define the schedule.
CARTO leverages native scheduling capabilities on each data warehouse to provide this functionality in all CARTO Data Warehouse, BigQuery, Snowflake and PostgreSQL connections.
March 14th, 2024
Improvements Builder
Maps created with CARTO Builder can now be embedded anywhere — even when they're not shared publicly. With private embedding you can restrict and maintain control over who can view these maps when embedded on web pages or apps.
To leverage private embedding simply share your map with the organization or with the specific groups you want to share the map with. These users need to be previously logged-in to CARTO to view the embedded map. Learn more at our Embedding maps documentation.
February 29th, 2024
New Workflows
During the last few weeks, we’ve been progressively adding new and improved components in CARTO Workflows:
Case When component for supporting column values based on conditional expressions.
Edit Schema component (replacing Refactor Columns): clean schemas, rename and cast columns.
Added ‘Append’ mode to Save as Table.
Added ‘Maximum distance’ setting to K-Nearest Neighbors.
Added Extract from JSON for extracting values from JSON columns using the native syntax from each data warehouse.
Added ‘Mode’ setting to H3 Polyfill and Quadbin Polyfill components.
Subdivide to split larger geometries into easier-to-process smaller features.
New UI for Draw Custom Features component
Composite Score Supervised: Create composite scores with the supervised method using this component. Take a look at the example template.
Composite Score Unsupervised: Create composite scores with the supervised method using this component. Take a look at the example template
February 21st, 2024
Improvements Builder
Exciting news – CARTO Builder has expanded its URL parameter capabilities to include widgets, SQL parameters, search locations, and feature selections. Now, when viewers interact with these elements, the URL updates in real time, making it easier to share customized map views. This update opens up possibilities for creating varied views from a single map, simplifying sharing, and minimizing the need for multiple map versions. It also enhances the embedding of maps into websites or apps, providing a seamless user experience without unnecessary redirections.
February 19th, 2024
Improvements Workspace
We have added a new column to the Users and Groups table of the Organization Settings which displays the authentication method used by each user (Google Account, Username/Password, SSO or Github). This will help Admins better manage their organization, avoid confusion and identify users quickly.
February 8th, 2024
New Workflows
We are excited to announce the release of a comprehensive set of new features in CARTO Workflows designed to provide the ability to trigger the execution of your workflows by calling an API.
Variable definition: Define variables that can be used within components' settings. These variables can also be configured as parameters, allowing for inputing dynamic values during API calls.
Expression support: Introducing expressions! Embed logic directly into component settings, enabling the use of SQL operators in conjunction with variable and column values from your data.
API endpoint for triggering workflows: Enable an API endpoint to initiate a workflow execution. This endpoint exposes all parameters set as variables, facilitating smooth integration.
Workflow status polling: Easily monitor the status of workflow execution.
Output definition and storage: Define the output of a workflow API execution, which will be stored in a temporary table. The Fully Qualified Name (FQN) of this table is included in the API response for effortless access post-execution. This output can be used along with other options like exporting result to a bucket, saving to a static table or send an email with the result.
Controlled caching behavior: Have control over caching behavior across all execution modes: UI, Scheduled (Beta), and via API.
All these elements have been built to enable users to integrate workflows into larger analytical processes, and to embed asynchronous analytical capabilities into web applications.
January 15th, 2024
Improvements Workspace
We have released a set of improvements that affect the experience of new users when they open a shared map or shared workflow for the first time. Previously, you had to invite those users or have them sign up manually. Now:
If your organization uses Single Sign-On (SSO), all maps and workflows shared links will redirect to your SSO login page for easier adoption and onboarding of new users
The unauthenticated screen for all shared maps and workflows has been redesigned for clarity
Users can now login or signup through the map/workflow link, and they will be automatically redirected to the desired map/workflow once successfully authenticated
January 11th, 2024
New Documentation
We are happy to announce the launch of our new CARTO Academy, with detailed tutorials, videos and templates to boost your spatial analysis skills and make you a proficient user of the CARTO platform.
Among others, in this new CARTO Academy you will find materials to get you started with Spatial Indexes, tutorials to help you build stunning visualizations and spatial analyses with CARTO Builder, step-by-step tutorials and templates for Workflows, and guides to develop your advanced spatial analysis skills with Google BigQuery, Snowflake and AWS Redshift.
Learn about the latest features, improvements and bug fixes in our product.
This page will always reflect the latest changes in the CARTO platform. Older release notes will be archived and are available in the left menu and the search bar.
March 19th, 2025
Improvement Builder
March 19th, 2025
New CARTO for Developers
As organizations expand their usage of CARTO and break the GIS data silo using cloud-native maps and workflows, it becomes important to have the right tools to manage all resources at scale. This is why, starting today, all users in CARTO have access to a new set of API endpoints where they can programmatically list and delete their maps, workflows, and connections.
Additionally, to empower Superadmins on their journey to enable CARTO for large organizations, we're exposing the following functionality via the new APIs:
List all the maps, workflows, and connections in a CARTO organization
Bulk delete of multiple assets with a single API request
Transfer the ownership of an asset (map, workflow, or connection) to another user
March 13th, 2025
New Workspace
Users can now connect to their Databricks account using OAuth authentication, with both Machine-to-Machine (M2M) and User-to-Machine (U2M) authentication flows supported! This adds an extra layer of security for Databricks users since OAuth tokens are automatically refreshed by default and do not require the direct management of the access token. For these reasons, Databricks is strongly recommending its users to choose OAuth over Personal Access Tokens.
February 27th, 2025
New Builder
Raster visualization is now available in Builder, marking a major milestone in CARTO’s end-to-end support for raster data. With this release, you can seamlessly import, analyze, and visualize raster datasets stored in Google BigQuery, Snowflake and Databricks—all within CARTO.
February 20th, 2025
New Builder
✨ Stay tuned—this is just the beginning. We’re already working on making AI Agents faster, smarter, and more powerful to elevate your mapping experience even further.
February 18th, 2025
New Workflows
We’re introducing Location Data Services (LDS) support and new data enrichment components in CARTO for Databricks, enabling more seamless geospatial analysis across different user roles and workflows.
Data Enrichment Components: These new Workflows components simplify use cases like demographic enrichment, POI data integration, and trade area analysis. Users can enhance datasets with information from CARTO’s Data Observatory or their own geospatial sources, whether structured as spatial indexes, points, or polygons. By embedding enrichment within CARTO Workflows, users can more easily integrate this step into their existing analysis.
These updates further reduce complexity for Databricks users working with spatial data. Data scientists can leverage LDS functions directly within their Databricks environment, while Workflows opens up more advanced spatial analysis to less technical users. By bringing LDS and enrichment into CARTO Workflows, we make it easier to build complete geospatial pipelines without writing custom code.
February 6th, 2025
New Integrations
Simply connect your cloud data warehouse to CARTO, install the QGIS plugin, and gain full control over your geospatial data in a familiar GIS environment. This enables smooth workflows for spatial data management, enrichment, and analysis while ensuring your data remains centralized and up to date in your cloud ecosystem.
February 3rd, 2025
New Workspace
Snowflake users can now connect to their Data Warehouse using Key-pair authentication! This is a much more secure alternative to basic username/password authentication as it is highly resistant to brute-force attacks, eliminates password management complexities, and can be easily used as the authentication mechanism for scripts and applications.
January 30th, 2025
New Builder
Are you working with datasets where multiple rows share the same geometry but have varying attributes, such as administrative boundaries, roads, or infrastructure locations?
With this update, you can:
Aggregate geometries in your layer to ensure optimal performance.
Aggregate styling and interaction attributes to retrieve relevant information linked to your aggregated feature.
Maintain widget functionality over the original source, enabling drill-down operations for deeper analysis.
January 30th, 2025
New Workflows
With this new release, users and partners can now extend the capabilities of our low-code analytics tool CARTO Workflows by creating, integrating and distributing custom components tailored to their specific spatial analytics needs.
Additionally, we have published a set of extensions readily available to be installed from the Workflows UI. The initial release boasts a curated collection of extensions, including:
January 17th, 2025
New Workspace
We've introduced the ability to share maps with individual users! Previously, maps could only be shared with your entire organization, specific user groups, or made publicly accessible via a link.
January 10th, 2025
New Workspace
We’re excited to announce that CARTO now supports connecting to Google BigQuery via Workload Identity Federation! This new capability enables secure, seamless authentication without requiring service account keys, making it easier to manage access and improving security for your cloud-native maps, workflows and applications.
With Workload Identity Federation, you can set up a trust relationship between CARTO and your Google Cloud projects for a smooth integration — In other words, you will be managing permissions to each of your CARTO users directly in Google Cloud, using IAM rules.
Another benefit of this method is that it provides a framework to effortlessly scale and distribute granular permissions across large-scale teams using CARTO and BigQuery. To get started:
January 7th, 2025
Improvement CARTO for Developers
Today, we're extremely happy to announce that developers can now build completely custom widgets using spatial index sources as well. These sources aggregate the data in a spatial index system, such as H3 or Quadbin, for increased performance and scalability. The main benefits of the new framework-agnostic widgets apply to spatial index-based widgets as well:
Build anything using H3 and Quadbin sources: from scorecards to bar charts, tables, time series, and everything in between.
Bring your own UI: Use your favorite charting library or custom HTML components.
Easily sync your widgets with the deck.gl map.
Seamlessly use widgets to filter the map and other widgets, fully leveraging your cloud data warehouse computing power.
Built using JS and Typescript only, they are fully compatible with the framework of your choice (Angular, React, Vue...), adding minimal dependencies.
December 13th, 2024
Improvement Workspace
December 4th, 2024
Improvement Data Observatory
We’re thrilled to announce a major update to the CARTO Data Observatory catalog! The new version introduces a completely redesigned interface, making it easier than ever to browse and discover spatial datasets. Whether you're searching for demographic insights, mobility or environmental data, the improved catalog helps you navigate a vast array of options with greater clarity and efficiency.
In addition to the new design, the updated catalog now includes richer metadata for each dataset. You can access detailed descriptions, links to product documentation, Frequently Asked Questions, and relevant use-cases for each product, enabling more informed decision-making when assessing external datasets to enrich your geospatial analysis.
November 24th, 2024
New CARTO for Developers
There are no trade-offs between simplicity, flexibility and security: developers using CARTO can now use Named Sources to avoid exposing the SQL queries used under the hood in their applications, and without necessarily having to add additional backend or proxy services.
November 21st, 2024
New Deployment Methods
You can now deploy your own instance of CARTO fully inside of Snowflake, as a Native App using Snowflake-managed Container Services.
From additional security benefits (from a closed environment within Snowflake) to streamlined installation, there are multiple reasons to be excited about this new deployment method, currently in BETA for specific customers.
November 21st, 2024
New Builder
Builder users can now modify the location or connection of data sources directly in Builder without breaking the map configuration. This ensures that maps retain their overall configuration, as long as the fields in the updated data source have the same name and type.
For map components such as style properties, widgets, or interactions that rely on properties not found in the updated data source, the configuration will gracefully fall back to its default settings, ensuring the map remains functional.
November 11th, 2024
New Builder
Admin users can now define custom color palettes for their CARTO organization, removing the need to manually add custom color styling in each new Builder map individually. This is a quick and easy way to apply styles consistently across various maps, available to all Editors within an organization.
October 31st, 2024
New Workflows
We are thrilled to announce that CARTO Workflows now supports direct connections to Databricks, significantly enhancing our integration capabilities for the Databricks platform. This new feature empowers Databricks' vast community of data engineers, data scientists, and analysts to seamlessly perform geospatial analysis within CARTO Workflows.
This release caps off a series of Databricks-focused updates rolled out over recent months:
By embedding these tools directly in Databricks, we are breaking down the geospatial data silo, making geospatial insights more accessible and actionable for enterprise teams.
October 17th, 2024
Improvement Builder
You can now easily search for specific features within the Table Widget, making them quick to locate. Hover over a table row to instantly highlight the corresponding feature on the map, and with a click, the map will automatically zoom to and center on that feature.
We’ve also improved the widget’s configuration, allowing you to label, format, and reorder columns without altering your data source.
October 15th, 2024
New Builder
_carto_point_density
propertyOctober 14th, 2024
New Builder, CARTO for Developers
October 9th, 2024
New CARTO for Developers
Developers using CARTO + deck.gl are scaling and accelerating their geospatial apps with powerful layers, using live data from their cloud data warehouse. Now, they can also add scalable, interactive charts and widgets to their geospatial applications.
Use flexible and scalable data models to achieve exactly and quickly what you need: From scorecards to bar charts, tables, time series, and everything in between.
Bring your own UI: Use your favorite charting library or custom HTML components.
Easily sync your widgets with the deck.gl map.
Seamlessly use widgets to filter the map and other widgets, fully leveraging your cloud data warehouse computing power.
Built with JS and Typescript, they are fully compatible with the framework of your choice (Angular, React, Vue...), adding minimal dependencies.
October 7th, 2024
New Builder
We've introduced a new functionality in Builder to dynamically visualize your point data as clusters, helping you gain deeper insights and uncover trends more effectively. By aggregating point data into clustering, you can:
Reduce Visual Clutter: Automatically group nearby points into clusters as you zoom out, helping you maintain clarity and readability, even with dense datasets.
Enhanced Performance: Clustering improves performance by reducing the number of individual features rendered, making it easier to handle large datasets without compromising speed.
Meaningful Aggregation: See patterns emerge as points are grouped into clusters, helping you identify hotspots, trends, and areas of interest quickly and effectively.
Interactive Exploration: As you zoom in and out, clusters dynamically adjust, revealing individual points as you get closer, giving you seamless interaction with your data at different scales.
October 4th, 2024
New Accounts
We've introduced a new toggle in the settings that allows Admins to enforce SSO within their organization. When enabled, every single user in that organization will have to authenticate using Single Sign-On, regaldless of their role. Users that try to authenticate with other mechanisms, such as User/Password and Google Account will not be allowed to log in.
September 26th, 2024
New Workflows
We’re pleased to introduce several updates to Workflows designed to improve both functionality and security:
Export Data from Any Node
Enhanced Security for Enterprise-Ready Components
In line with our ongoing platform-wide security initiative, we've implemented the following updates:
You can now specify a custom bucket location when using the Send by Email component, giving you control over where your data is sent.
These updates make Workflows an even more powerful tool for enterprise users while maintaining a focus on security and ease of use.
September 11th, 2024
New Workspace
Users can now require viewer credentials for their Snowflake OAuth connections. When this option is enabled, anyone accessing data through the connection — whether they're consuming maps, running workflows, or using the data explorer — will need to authenticate with their own credentials.
August 30th, 2024
New Workspace
Administrator can now easily export (manually or programmatically via API) a comprehensive data collection of everything that happened within their CARTO organization.
The new Activity Data can be then analyzed to deeply understand things like:
Basic engagement indicators: weekly active users, workflows run per week...
Most used features: most used workflow components...
Quota consumption: who is consuming more quota and why
And many more insights about your CARTO organization
July 31st, 2024
Improvement Workspace
We've introduced an improved flow for transferring user assets (maps, workflows, connections, etc) when deleting a user or when downgrading an Editor/Admin to Viewer. From now on, Admins will be able to select the specific user that will receive the assets.
July 31st, 2024
New Builder
We've introduced a new functionality in Builder to dynamically visualize your point data as H3 aggregations, helping you gain deeper insights and uncover trends more effectively.
By aggregating point data, you can:
Simplify Complex Data: Aggregate large volumes of point data into meaningful patterns and trends, making it easier to interpret and analyze.
Enhance Performance: Improve rendering times and performance, especially with large datasets, by reducing the number of individual points displayed.
Identify Hotspots: Quickly identify areas of high density or activity, helping you make data-driven decisions.
Visual Clarity: Reduce visual clutter by grouping nearby points, providing a clearer and more informative map visualization.
Simply select this new visualization type and enjoy the benefits of aggregated data visualization, all with exceptional performance thanks to CARTO's native support for spatial indexes.
July 29th, 2024
Improvement Builder
We are excited to introduce an improved panel in Builder for configuring your layers. This update significantly enhances the UI and UX of this panel, making the experience of creating visualizations in Builder even more enjoyable and efficient.
The redesigned panel features a cleaner layout and includes a new 'Data' section at the top. In this new section, you can define the spatial definition of the data source linked to your layer. This is especially useful if your source contains multiple spatial columns or if Builder cannot recognize the spatial column by looking at our default conventions.
July 29th, 2024
New Workflows
Integrated Collection: Access a wide range of workflow templates hosted on the CARTO Academy website, now readily available in the CARTO Workspace.
Simplified Process: Users no longer need to visit the Academy site to download and import templates. The new feature allows you to easily recreate templates by selecting ‘New Workflow > From template’ within the Workspace.
Enhanced Usability: This integration ensures that all available templates can be accessed with just a few clicks, promoting best practices and facilitating quicker setup of workflows.
This feature is designed to ease the learning curve by providing immediate access to valuable workflow templates that illustrate both building blocks for common geospatial analytics and more complex use cases, like industry-specific analysis for Telco, Insurance, Retail and CPG, Out of Home advertising, etc
July 19th, 2024
New Builder
We are excited to introduce the zoom to layer functionality in Builder, which allows you to easily zoom to the layer extent, providing an immediate view of your dataset. When layers are filtered by widgets or parameters, the zoom focuses on the filtered data, ensuring you see exactly what's relevant.
Additionally, we have incorporated a "Show only this/Show all layers" feature, allowing you to quickly toggle all layers on and off with a single action, especially useful for maps including multiple layers.
July 15th, 2024
New Workflows
We are excited to introduce a powerful new set of components in Workflows that significantly enhance your geospatial data processing capabilities. These components are designed to facilitate the creation of various types of tilesets, allowing for efficient visualization and analysis of large spatial datasets. Here are the key features:
Create Vector Tileset
Generate vector tilesets from point, line, or polygon tables, enabling smooth and interactive map experiences.
Create Point Aggregation Tileset
Aggregate point data along with their properties into tilesets, ideal for visualizing dense point data on maps.
Create Quadbin Aggregation Tileset
Generate tilesets by aggregating quadbin indices, providing a fast and scalable way to manage spatial hierarchies and visualize large datasets.
Create H3 Aggregation Tileset
Utilize H3 hexagonal indexing to create aggregated tilesets, perfect for detailed spatial analysis and representation.
June 28th, 2024
New Workspace
We are happy to announce a new system that allows users to classify and filter maps and workflows in the CARTO Workspace with tags. With this new feature, editor users will be able to create, apply and filter maps and workflows by tags, considerably improving the organization of assets within CARTO. With this new enhancement:
You can create, apply and remove tags by editing the Map/Workflow properties from the Workspace;
We have added a tag filter to the Workspace so you can filter by one or several tags;
Once a tag filter is applied, you can copy the URL for sharing that Workspace view internally;
Tags will be automatically removed when they are no longer applied to any map or workflow.
June 28th, 2024
New Analytics Toolbox
This release includes procedures for:
Path profile analysis to evaluate the line of sight and identify potential obstructions between two points;
June 28th, 2024
New Analytics Toolbox
June 20th, 2024
New Builder, CARTO for Developers
We are thrilled to announce density heatmap visualization supporting very large scale point-based datasets! This new feature allows you to render massive point datasets as a heatmap in a scalable and performant manner. Available now in Builder, you can easily identify hotspot patterns and gain insights from your data.
June 19th, 2024
New Workflows
We are excited to introduce enhanced data importing capabilities in CARTO Workflows. This new release includes a variety of features designed to simplify and expand the ways you can import data into your workflows, providing greater flexibility and functionality.
Import from URL Component
Sunset of Previous Method
Quick Import from your desktop
Files uploaded in this manner remain accessible within each workflow, ensuring consistent data availability and management.
June 3rd, 2024
New Builder
We are thrilled to announce a powerful new feature for Workflows: the ability to connect your workflows with external API services. With this new capability, we enabling use cases like the following:
Retrieve Data from External APIs: Augment your datasets by pulling in information from APIs such as Google Environment APIs, government, cadaster, parcel data, and other specialized data sources.
Trigger Actions via API: Automatically trigger external processes, send notifications, or execute commands directly from your workflows, like:
Notify on chat applications: Send real-time notifications to your company's channels to keep your team updated on workflow executions.
Integrations with automation tools: Integrate with automation tools to trigger external actions from a Workflow execution.
Send data from your Workflows to external APIs: Use data from any node in your workflow to build the body for a request.
May 16th, 2024
New Builder
A basemap is a crucial component of any map, providing essential context, spatial features, and the visual foundation for your creations. To meet the unique needs of each organization, we now enable you to bring your own basemap directly into your CARTO organization.
May 14th, 2024
Improvement Builder
We are excited to introduce a set of enhancements in CARTO Builder designed to further improve the performance of our interactive map visualizations. With these improvements, Builder will:
Load only essential properties: Builder will now load only the essential properties from your tables or SQL queries when they are needed in the map. This reduces unnecessary data transfer and speeds up processing.
Reduce tile requests: The number of tile requests has been significantly reduced, resulting in faster map loading times and a smoother user experience.
Limit simultaneous queries: To enhance stability and prevent overload, Builder will limit the number of simultaneous queries, ensuring a more reliable performance.
These enhancements are part of our ongoing commitment to providing the best possible experience with CARTO Builder.
April 29th, 2024
Improvement Workspace
We believe that all paths to success start from the CARTO Workspace, and the path to successfully developing powerful geospatial apps isn't an exception. With this in mind, we've carefully redesigned the experience when accessing the Developers section, and these are the highlights:
This change unifies the management of API Access Tokens and OAuth Clients (previously known as Applications) in a single section, making more clear what each method is best for.
April 24th, 2024
New Workspace, Workflows
We're happy to introduce a suite of powerful new features that are set to enable working with raster data in CARTO. Before these were available, working with raster data required using external CLI applications and dealing with SQL queries manually in order to leverage the analytical capabilities of the CARTO Analytics Toolbox for Snowflake and BigQuery.
Raster Tables in Data Explorer: Dive deeper into your raster data in the data warehouse with full support for raster tables in the Data Explorer. Gain access to a specific set of metadata and custom actions for raster tables.
April 17th, 2024
New Builder
We’ve launched a new feature that allows you to download detailed PDF reports of your interactive Builder maps. These reports capture everything from the current map extent to widgets, parameters, and the map description.
Whether you're sharing insights with colleagues, presenting to stakeholders, or documenting your analysis, this new feature packs the richness of your interactive maps into a portable, easy-to-share format.
April 11th, 2024
Improvement Workspace
A new AI-powered help assistant can now be found in the Help sidebar, available at all times from CARTO Workspace, Builder and Workflows.
It will provide quick answers based on our documentation and will link to the most relevant resource. With our documentation evolving and growing in size and depth, this AI-powered tool will save precious time and will guide you in the right direction without leaving CARTO. Ask anything!
March 31st, 2024
New Builder
March 27th, 2024
New CARTO for Developers
March 21st, 2024
New Workflows
Hours: The workflow will be executed every X hours, at o'clock times.
Days: The workflow will be executed every day at a specific time.
Weeks: The workflow will be executed weekly, on a specific day, at a specific time.
Months: The workflow will be executed monthly, on a specific day, at a specific time.
Custom: Use a custom expression to define the schedule.
CARTO leverages native scheduling capabilities on each data warehouse to provide this functionality in all CARTO Data Warehouse, BigQuery, Snowflake and PostgreSQL connections.
March 14th, 2024
Improvements Builder
Maps created with CARTO Builder can now be embedded anywhere — even when they're not shared publicly. With private embedding you can restrict and maintain control over who can view these maps when embedded on web pages or apps.
February 29th, 2024
New Workflows
During the last few weeks, we’ve been progressively adding new and improved components in CARTO Workflows:
February 21st, 2024
Improvements Builder
February 19th, 2024
Improvements Workspace
We have added a new column to the Users and Groups table of the Organization Settings which displays the authentication method used by each user (Google Account, Username/Password, SSO or Github). This will help Admins better manage their organization, avoid confusion and identify users quickly.
February 8th, 2024
New Workflows
We are excited to announce the release of a comprehensive set of new features in CARTO Workflows designed to provide the ability to trigger the execution of your workflows by calling an API.
All these elements have been built to enable users to integrate workflows into larger analytical processes, and to embed asynchronous analytical capabilities into web applications.
January 15th, 2024
Improvements Workspace
The unauthenticated screen for all shared maps and workflows has been redesigned for clarity
Users can now login or signup through the map/workflow link, and they will be automatically redirected to the desired map/workflow once successfully authenticated
January 11th, 2024
New Documentation
December 20th, 2023
Improvement Builder
Additionally, when exporting data as CSV, it now includes the geometry column in WKT (Well-Known Text) format, if applicable. This enhancement simplifies data handling and boosts compatibility with various geospatial tools, making integrations smoother.
December 18th, 2023
New Workflows
It can be used to interpret results from an analysis, using different variables on your data to compose a prompt that will be evaluated on each row.
With this new addition, Generative AI capabilities are handy and readily available from Workflows.
November 28th, 2023
New Workflows
The node's data will be exported as a series of files, which URLs will be stored in a table. Just inspect the Data tab in the results panel to access the links to each file.
This component is currently available for all CARTO Data Warehouse and BigQuery connections.
November 27th, 2023
Improvement Workflows
We have added a few improvements to the results panel in Workflows that are focused on better usability and data exploration capabilities:
Renovated, sleeker design;
See when the workflow was executed for the last time;
Easily find the number of columns and rows of a result;
Explore the complete result with pagination for optimal performance;
Copy the content of a page to the clipboard, ready to be pasted into a spreadsheet;
Analyze statistics of each column:
Frequency of the Top 20 categories for string, date and timestamp columns;
Maximum and minimum values, average and sum for numeric columns.
SQL control code is hidden to facilitate readability.
New features and improvements introduced from January to March 2023
March 28th, 2023
Improvement Workspace
In order to give more flexibility to our users, we have removed a lot of the quotas that were feature-specific, such as maps, public maps, apps or connections, and we have replaced them with a combined usage metric, the Usage quota, that will be the main driver of consumption for all new customers through the CARTO platform.
Additionally, we have changed the way LDS credits are calculated. Before, they were monthly and separated by service: geocoding and isolines. Now, we've combined them into a single annual quota that results in more capacity and better flexibility.
March 14th, 2023
Improvement Workspace
With this new functionality, CARTO users working with Amazon Redshift will be able to quickly get their geospatial data ready for advanced analysis and visualization, from no-code tools like Builder or Workflows to geospatial development libraries such as CARTO for deck.gl.
March 7th, 2023
New Analytics Toolbox
March 3rd, 2023
New Builder
A set of new analyses have been added to Builder, to reach the same level of support on different data warehouses:
Create buffers: Available now for Redshift and Snowflake connections.
Intersect and aggregate: Available now for Snowflake connections.
K-means clustering: Available now for Redshift and Snowflake connections.
February 24th, 2023
Beta Workflows
Additionally, we have adapted the Workflows main page in the Workspace to allow searching workflows and managing the existing ones, in line with what’s available in the Maps section.
February 1st, 2023
Beta Workflows
January 31st, 2023
Beta Analytics Toolbox
January 31st, 2023
Beta Analytics Toolbox
January 26th, 2023
New Workspace
The new dataset "private" is a unique dataset for each user, and all the tables and tilesets in this dataset will only be available to that user. Private datasets have a unique qualified name that identifies the user, extracted from their email.
January 24th, 2023
Improvement Builder Workspace
An important step of most processes in CARTO is to browse and select data sources and data locations:
This is especially beneficial if you have a large number of projects/databases, schemas/datasets, or tables and tilesets: where previously you would need to scroll indefinitely, now you can perform a quick search.
January 23rd, 2023
New Builder
This produces a very significant increment in performance, but also allows aggregating data from the original features to make sure that all data is taken into consideration.
Some highlights:
Available for all point tile layers from all data warehouses
Implemented with pure SQL in our Maps API, no external dependencies such as the Analytics Toolbox or third-party libraries.
It allows aggregating properties from the original points and also the number of points per cell.
Inherits all the advantages and features of the previously existing Spatial Index layers.
The aggregation happens transparently so no need to manually type any SQL code to aggregate the points.
January 18th, 2023
Beta Workflows
January 18th, 2023
Improvement Documentation
Our documentation portal just got a new look and feel! This new layout should provide the following benefits:
Cleaner look that uses more screen space if available
A search bar to quickly find content
All the documentation is organized and available on the left menu
All pages now have an "On this page" index on the right sidebar __ to quickly locate sections
January 13th, 2023
New Builder
Collaborating on CARTO maps is finally possible, in asynchronous mode. This is helpful in setups such as production-ready maps, where the original owner might be out of the office; or in situations where the data, analysis, and cartography are each accomplished by different users. This is how it works:
The map owner first needs to enable collaboration for that map.
From that moment, all editors with access to the map will be able to edit it.
If two editors try to edit at the same time, the last one will be locked out, with the option of requesting to take over editing.
Happy collaboration!
New features and improvements introduced from April to June 2024
June 28th, 2024
New Workspace
We are happy to announce a new system to allow users to classify and filter maps and workflows in the CARTO Workspace with tags. With this new feature, editor users will be able to create, apply and filter maps and workflows by tags, considerably improving the organization of assets within CARTO. With this new enhacement:
You can create, apply and remove tags by editing the Map/Workflow properties from the Workspace.
We have added a tag filter to the Workspace so you can filter by one or several tags.
Once a tag filter is applied, you can copy the URL for sharing that Workspace view internally.
Tags will be automatically removed when they are no longer applied to any map or workflow.
June 28th, 2024
New Analytics Toolbox
This release includes procedures for:
Path profile analysis to evaluate the line of sight and identify potential obstructions between two points;
June 28th, 2024
New Analytics Toolbox
June 20th, 2024
New Builder, CARTO for Developers
We are thrilled to announce density heatmap visualization for vast point datasets! This new feature allows you to render large point datasets as a heatmap in a scalable and performant manner. Available now in Builder, you can easily identify hotspot patterns and gain insights from your data.
June 19th, 2024
New Workflows
We are excited to introduce enhanced data importing capabilities in CARTO Workflows. This new release includes a variety of features designed to simplify and expand the ways you can import data into your workflows, providing greater flexibility and functionality.
Import from URL Component
Sunset of Previous Method
Quick Import from your desktop
Files uploaded in this manner remain accessible within each workflow, ensuring consistent data availability and management.
June 3rd, 2024
New Builder
We are thrilled to announce a powerful new feature for Workflows: the ability to connect your workflows with external API services. With this new capability, we enabling use cases like the following:
Retrieve Data from External APIs: Augment your datasets by pulling in information from APIs such as Google Environment APIs, government, cadaster, parcel data, and other specialized data sources.
Trigger Actions via API: Automatically trigger external processes, send notifications, or execute commands directly from your workflows, like:
Notify on chat applications: Send real-time notifications to your company's channels to keep your team updated on workflow executions.
Integrations with automation tools: Integrate with automation tools to trigger external actions from a Workflow execution.
Send data from your Workflows to external APIs: Use data from any node in your workflow to build the body for a request.
May 16th, 2024
New Builder
A basemap is a crucial component of any map, providing essential context, spatial features, and the visual foundation for your creations. To meet the unique needs of each organization, we now enable you to bring your own basemap directly into your CARTO organization.
May 14th, 2024
Improvement Builder
We are excited to introduce a set of enhancements in CARTO Builder designed to further improve the performance of our interactive map visualizations. With these improvements, Builder will:
Load only essential properties: Builder will now load only the essential properties from your tables or SQL queries when they are needed in the map. This reduces unnecessary data transfer and speeds up processing.
Reduce tile requests: The number of tile requests has been significantly reduced, resulting in faster map loading times and a smoother user experience.
Limit simultaneous queries: To enhance stability and prevent overload, Builder will limit the number of simultaneous queries, ensuring a more reliable performance.
These enhancements are part of our ongoing commitment to providing the best possible experience with CARTO Builder.
April 29th, 2024
Improvement Workspace
We believe that all paths to success start from the CARTO Workspace, and the path to successfully developing powerful geospatial apps isn't an exception. With this in mind, we've carefully redesigned the experience when accessing the Developers section, and these are the highlights:
This change unifies the management of API Access Tokens and OAuth Clients (previously known as Applications) in a single section, making more clear what each method is best for.
April 24th, 2024
New Workspace, Workfows
We're happy to introduce a suite of powerful new features that are set to enable working with raster data in CARTO. Before these were available, working with raster data required using external CLI applications and dealing with SQL queries manually in order to leverage the analytical capabilities of the CARTO Analytics Toolbox for Snowflake and BigQuery.
Raster Tables in Data Explorer: Dive deeper into your raster data in the data warehouse with full support for raster tables in the Data Explorer. Gain access to a specific set of metadata and custom actions for raster tables.
April 17th, 2024
New Builder
We’ve launched a new feature that allows you to download detailed PDF reports of your interactive Builder maps. These reports capture everything from the current map extent to widgets, parameters, and the map description.
Whether you're sharing insights with colleagues, presenting to stakeholders, or documenting your analysis, this new feature packs the richness of your interactive maps into a portable, easy-to-share format.
April 11th, 2024
Improvement Workspace
A new AI-powered help assistant can now be found in the Help sidebar, available at all times from CARTO Workspace, Builder and Workflows.
It will provide quick answers based on our documentation and will link to the most relevant resource. With our documentation evolving and growing in size and depth, this AI-powered tool will save precious time and will guide you in the right direction without leaving CARTO. Ask anything!
New features and improvements introduced from October to December 2024
December 13th, 2024
Improvement Workspace
December 4th, 2024
Improvement Data Observatory
We’re thrilled to announce a major update to the CARTO Data Observatory catalog! The new version introduces a completely redesigned interface, making it easier than ever to browse and discover spatial datasets. Whether you're searching for demographic insights, mobility or environmental data, the improved catalog helps you navigate a vast array of options with greater clarity and efficiency.
In addition to the new design, the updated catalog now includes richer metadata for each dataset. You can access detailed descriptions, links to product documentation, Frequently Asked Questions, and relevant use-cases for each product, enabling more informed decision-making when assessing external datasets to enrich your geospatial analysis.
November 24th, 2024
New CARTO for Developers
There are no trade-offs between simplicity, flexibility and security: developers using CARTO can now use Named Sources to avoid exposing the SQL queries used under the hood in their applications, and without necessarily having to add additional backend or proxy services.
November 21st, 2024
New Deployment Methods
You can now deploy your own instance of CARTO fully inside of Snowflake, as a Native App using Snowflake-maaged Container Services.
From additional security benefits (from a closed environment within Snowflake) to streamlined installation, there are multiple reasons to be excited about this new deployment method, currently in BETA for specific customers.
November 21st, 2024
New Builder
Builder users can now modify the location or connection of data sources directly in Builder without breaking the map configuration. This ensures that maps retain their overall configuration, as long as the fields in the updated data source have the same name and type.
For map components such as style properties, widgets, or interactions that rely on properties not found in the updated data source, the configuration will gracefully fall back to its default settings, ensuring the map remains functional.
November 11th, 2024
New Builder
Admin users can now define custom color palettes for their CARTO organization, removing the need to manually add custom color styling in each new Builder map individually. This is a quick and easy way to apply styles consistently across various maps, available to all Editors within an organization.
October 31st, 2024
New Workflows
We are thrilled to announce that CARTO Workflows now supports direct connections to Databricks, significantly enhancing our integration capabilities for the Databricks platform. This new feature empowers Databricks' vast community of data engineers, data scientists, and analysts to seamlessly perform geospatial analysis within CARTO Workflows.
This release caps off a series of Databricks-focused updates rolled out over recent months:
By embedding these tools directly in Databricks, we are breaking down the geospatial data silo, making geospatial insights more accessible and actionable for enterprise teams.
October 17th, 2024
Improvement Builder
You can now easily search for specific features within the Table Widget, making them quick to locate. Hover over a table row to instantly highlight the corresponding feature on the map, and with a click, the map will automatically zoom to and center on that feature.
We’ve also improved the widget’s configuration, allowing you to label, format, and reorder columns without altering your data source.
October 15th, 2024
New Builder
_carto_point_density
propertyOctober 14th, 2024
New Builder, CARTO for Developers
October 9th, 2024
New CARTO for Developers
Developers using CARTO + deck.gl are scaling and accelerating their geospatial apps with powerful layers, using live data from their cloud data warehouse. Now, they can also add scalable, interactive charts and widgets to their geospatial applications.
Use flexible and scalable data models to achieve exactly and quickly what you need: From scorecards to bar charts, tables, time series, and everything in between.
Bring your own UI: Use your favorite charting library or custom HTML components.
Easily sync your widgets with the deck.gl map.
Seamlessly use widgets to filter the map and other widgets, fully leveraging your cloud data warehouse computing power.
Built with JS and Typescript, they are fully compatible with the framework of your choice (Angular, React, Vue...), adding minimal dependencies.
7th October, 2024
New Builder
We've introduced a new functionality in Builder to dynamically visualize your point data as clusters, helping you gain deeper insights and uncover trends more effectively.
By aggregating point data into clustering, you can:
Reduce Visual Clutter: Automatically group nearby points into clusters as you zoom out, helping you maintain clarity and readability, even with dense datasets.
Enhanced Performance: Clustering improves performance by reducing the number of individual features rendered, making it easier to handle large datasets without compromising speed.
Meaningful Aggregation: See patterns emerge as points are grouped into clusters, helping you identify hotspots, trends, and areas of interest quickly and effectively.
Interactive Exploration: As you zoom in and out, clusters dynamically adjust, revealing individual points as you get closer, giving you seamless interaction with your data at different scales.
Editor users can now manage the presence of a layer in the map layer list directly from the tab in Builder. Previously, it was only possible to show or hide a layer’s legend. With this update, you now have full control over whether a layer itself should appear in the map layer list — what end-users see and interact with during map exploration.
Ready to scale up? Head over to our to get started.
Want to learn more? head over to our .
This new capability unlocks powerful use cases, allowing you to explore and analyze data at scale, seamlessly within your cloud environment, without additional data movement. Interesting in learning more? .
We’re excited to announce the Public Preview of CARTO , designed to make interacting with your maps in Builder more intuitive and dynamic. With AI Agents, users can seamlessly zoom to specific regions based on conversational input, explore map details, and apply filters using widgets—all through a natural language interface.
Location Data Services (LDS) Support: Now available in both the and as Workflows components. Users can perform , , and calculations via CARTO’s standard providers. The Analytics Toolbox enables direct use within Databricks notebooks and SQL workflows, while CARTO Workflows provides a low-code interface, integrating LDS into broader spatial analysis pipelines. LDS usage is subject to CARTO licensing and quotas, but users can also bring their own provider credentials, just as with other data warehouses.
The new allows you to access, visualize, and edit spatial data from leading cloud data warehouses directly within QGIS. With this plugin, you can seamlessly check out data from Google BigQuery, Snowflake, Databricks, AWS Redshift, and PostgreSQL, edit it within QGIS, and commit changes back to your data warehouse—all powered by the CARTO platform.
We've also added support for Key-pair rotation, enabling users to update the private key of Key-pair connections they own. For more information, see our section on .
The new functionality allows you to aggregate those features in your layer visualization and interactions, improving performance while keeping access to detailed insights.
To start creating your own Workflows Extension Packages we have published . Kick off your own repository out the template and start developing extensions for BigQuery and Snowflake connections.
: Integrate machine learning workflows with your geospatial data using BigQuery ML directly within Workflows.
: Unleash the power of Google Earth Engine for advanced spatial analysis tasks.
: Bring the power of Google Environment APIs (Solar, Air Quality, Pollen) into your geospatial analytics workflows.
: Analyze telecommunication signals with path profiles, propagation modeling, and obstacle identification.
Head over to the CARTO Workflows documentation to learn more about and explore the initial release offerings.
With this new feature, Editors now have more granular control over map access permissions. Users can select exactly which individuals should have access to a map (and they can revoke it at any time), making it easier to collaborate on specific projects while maintaining security. For more information, visit our section on .
Administrators will need to set up an .
Once the integration is set up, all users will be able to .
A few months ago we introduced our , a new system for developers to add scalable and highly-performant charts and other data components to their CARTO + deck.gl application, with support for vector-based data sources: points, lines and polygons.
Ready to learn more? Get started by reading the or by exploring the .
We've introduced several improvements to help Admins of organizations using SSO groups manage them more effectively. Admins can now view the composition of groups, search for specific users within them, and delete unused groups. Additionally, we've implemented a new method to synchronize only a subset of groups into CARTO. For more details, visit our article on .
today to explore the new Data Observatory catalog and unlock the full potential of your projects! Access more information about the Data Observatory in our .
Developers can manage their Named Sources manually via UI or programmatically via API. To get started with Named Sources, check the and the .
Learn more about in our documentation or read about it in our .
This functionality allows users to repurpose their maps effortlessly, even when the data source location in their data warehouse changes—eliminating the need to recreate maps from scratch.
Custom color palettes can be created from the Settings and are applied directly in CARTO Builder. For more information, see our article on .
We have introduced support for SQL Warehouses and Unity Catalog in .
Made Databricks connections available in across the platform, as well as geospatial applications developed with CARTO.
Enabled and for high-performance visualizations.
Workflows for Databricks leverages , and the to make geospatial analysis easier and more performant than ever for data scientists, engineers and analysts on Databricks. Being a cloud-native integration, CARTO pushes down all processing to Databricks, profiting from the massive computation capabilities.
Navigating large geospatial datasets is now faster with our upgraded , featuring search, highlight, and zoom capabilities.
Many times, a single basemap doesn't fully meet all of your mapping needs. Now, with the new in Builder, users can easily switch between different basemaps available in your organization. This feature allows you to tailor the visual context of your maps to specific use cases, enhancing the overall data exploration experience.
We’ve added a new styling property, _carto_point_density
, for point dynamic tiling sources, perfect for visualizing point density. You can use this property in Builder or your custom apps to style your points by radius, fill, or stroke color, making your maps more insightful and visually appealing. Learn more about it in our .
This is what we love about the new :
We're excited to see what you build! — To get started, head over to the or check the .
For more details, check out our section on .
A new button is now available on the Data tab for every executed node in your workflow canvas, allowing you to export data directly. This asynchronous export process can be tracked via the Activity Panel, similar to how .
now works without requiring attached data, offering more flexibility in workflow automation.
no longer uses public buckets. Users are now required to specify their own bucket locations, ensuring more secure data management.
This ensures that security policies set in the database, such as Row-Level Security, are enforced. For more details, visit our section on .
As organizations roll out CARTO to different teams and larger groups of users, it becomes increasingly important for administrators to understand and monitor how their organization is using CARTO, and this is now easy, powerful and flexible thanks to the new feature
Want to get started? Head over to the documentation. Make sure to also check the full , as well as the where we share practical guides and SQL queries on how to analyze this data.
For more information, check our documentation on and .
Learn more about the spatial definition of your sources . Also, explore our for layers to get the most out of this update.
We are pleased to announce the integration of our directly within the CARTO Workspace. This feature aims to streamline your workflow creation process, making it faster and more efficient to .
Whether you're exploring vast datasets or gathering insights on geospatially distributed features, these new features will ensure a better exploration experience! Learn more about this feature in our .
These new components enable you to transform your spatial data into highly efficient and scalable tilesets, which can be seamlessly integrated into your mapping applications. For more detailed information on how to use these components, visit .
We are thrilled to announce our new functions for line of sight and signal propagation analysis in the Analytics Toolbox for BigQuery. These new procedures, available in the module, enable network planners to run coverage analysis natively within BigQuery. With this functions users can now assess the geographical areas where current or potential new network's signal is available and evaluate its quality.
Path loss estimation of a signal as it propagates through an environment, with options for the and .
Learn more about these new features in our , and start testing them by following our step-by-step .
We are excited to announce the addition of two new space-time analyses available in the module of the Analytics Toolbox for BigQuery:
, to classify hotspots based on changes in their intensity over time, such as strengthening hotspots, declining hotspots, occasional hotspots, and more;
, to identify locations with similar temporal behaviors.
Learn more on how to perform these spatiotemporal analyses by exploring our tutorials for and .
Developers can also build their own large-scale heatmaps in their apps using CARTO + deck.gl, with the new heatmapTileLayer
(Experimental). Learn more from our and .
This allows users to import data directly from a public URL. It is compatible with BigQuery, Snowflake, Redshift, and PostgreSQL. By leveraging the CARTO Import API, this component ensures seamless data integration across different database systems.
The component supports workflows that run on a schedule or are executed via API, providing more robust and automated data management options.
The , which was limited to UI-based operations, will be deprecated. The new Import from URL component provides a more versatile and powerful alternative.
Users can now quickly from their computers directly into the workflow canvas. This feature supports drag-and-drop functionality, making it easier to integrate local files into your workflows.
Leverage all this new functionality by using the new component: A dedicated Workflows component that facilitates making requests to external APIs, providing enhanced versatility and extensibility. It uses the http_request
module from the CARTO Analytics Toolbox.
It also supports to embed logic directly into component settings using SQL operators combined with variable and column values.
Admin users at CARTO can now upload custom basemaps and tailor the basemap gallery options available to Editor users in Builder. Unleash your creativity and enjoy an enhanced map-making experience while maintaining a cohesive and consistent selection of basemaps throughout your organization. To learn more about how you can upload custom basemaps to the CARTO platform and the supported formats, check . For a step-by-step guide on custom basemaps, check out our in the Academy.
New Overview with a curated list of .
A simplified system to manage all your authentication methods.
A new list containing all your , for easy access.
Additionally, we've simplified the way that organizations decide the content in their section. Before, it was a mix of developer credentials and apps registered by the administrator. Now, administrators in CARTO are in full control of , including the visibility/sharing settings.
Developer credentials created before April 25th have been duplicated as applications to maintain the same visibility level as previously.
Import Cloud Optimized GeoTIFFs: We have made raster data ingestion processes a lot easier: with our latest enhancements, you can now effortlessly import Cloud Optimized GeoTIFFs to and via both the Import API and the . This provides a streamlined and efficient method for ingesting raster files into BigQuery and Snowflake, ensuring optimal storage efficiency and fast query access.
Workflow Components for Raster Analysis: Take your spatial analyses to the next level with our new Workflow components designed specifically for working with raster data sources. Whether you're looking to extract raster values or perform complex intersect and aggregate operations, our new components, including "" and "", provide you with the tools you need to unlock valuable insights from your raster datasets.
This new feature simplifies the map-making process by letting Editor users switch seamlessly between editing and previewing. With , these users can easily see how the map will look like to viewers, allowing them to review and refine it before sharing. This smooth workflow ensures that maps are well-presented and meet the highest standards of clarity and effectiveness.
Additionally we've enhanced our map-sharing functionality to deliver a smoother and more intuitive experience. This update focuses on streamlining the process of sharing maps with others, ensuring a more seamless interaction. Dive into the details of these improvements in our .
A new major version of deck.gl is out. deck.gl is the open-source visualization library that powers all CARTO visualizations, and one of the main components of .
For a complete changelog, visit the official .
To address breaking changes, read the official . Changes in the CARTO module are also addressed there.
We have also published a complete set of .
We're very happy to see CARTO joining efforts with many other contributors from the vis.gl and OpenJS Foundation communities. Read more about this release in the .
With this new capability, analytical pipelines created with Workflows can be so they are executed on a specific period:
To leverage private embedding simply share your map with the organization or with the specific groups you want to share the map with. These users need to be previously logged-in to CARTO to view the embedded map. Learn more at our .
component for supporting column values based on conditional expressions.
component (replacing Refactor Columns): clean schemas, rename and cast columns.
Added ‘Append’ mode to .
Added ‘Maximum distance’ setting to .
Added for extracting values from JSON columns using the native syntax from each data warehouse.
Added ‘Mode’ setting to and components.
to split larger geometries into easier-to-process smaller features.
New UI for component
: Create composite scores with the supervised method using this component. .
: Create composite scores with the supervised method using this component. template
Exciting news – CARTO Builder has expanded its capabilities to include widgets, SQL parameters, search locations, and feature selections. Now, when viewers interact with these elements, the URL updates in real time, making it easier to share customized map views. This update opens up possibilities for creating varied views from a single map, simplifying sharing, and minimizing the need for multiple map versions. It also enhances the embedding of maps into websites or apps, providing a seamless user experience without unnecessary redirections.
Variable definition: that can be used within components' settings. These variables can also be configured as parameters, allowing for inputing dynamic values during API calls.
Expression support: Introducing expressions! directly into component settings, enabling the use of SQL operators in conjunction with variable and column values from your data.
API endpoint for triggering workflows: to initiate a workflow execution. This endpoint exposes all parameters set as variables, facilitating smooth integration.
Workflow status polling: the status of workflow execution.
Output definition and storage: , which will be stored in a temporary table. The Fully Qualified Name (FQN) of this table is included in the API response for effortless access post-execution. This output can be used along with other options like exporting result to a bucket, saving to a static table or send an email with the result.
Controlled caching behavior: Have control over caching behavior across all execution modes: UI, , and via .
We have released a set of improvements that affect the experience of new users when they open a or for the first time. Previously, you had to invite those users or have them sign up manually. Now:
If your organization uses , all maps and workflows shared links will redirect to your SSO login page for easier adoption and onboarding of new users
We are happy to announce the launch of our new , with detailed tutorials, videos and templates to boost your spatial analysis skills and make you a proficient user of the CARTO platform.
Among others, in this new CARTO Academy you will find materials to get you started with , tutorials to help you build stunning and with CARTO Builder, step-by-step and for Workflows, and guides to develop your advanced spatial analysis skills with , and .
We've upgraded the in Builder maps, shifting the data export process to work in server-side mode for an enhanced efficiency and data integrity. This improvement ensures a more reliable data retrieval experience.
Looking to leverage this enhanced functionality for RDS for PostgreSQL data sources? Don't forget to set up the necessary S3 bucket integration to enable the export feature. For more details and guidance, check out our .
We have added a to Workflows that leverages capabilities to allow embedding Generative AI functionalities into your geospatial analytical pipelines.
We have just released a that allows exporting the result from any node in a workflow to a storage bucket.
Find all the documentation about these improvements .
The Usage quota is related to the number of successful API calls, excluding the metadata. Learn more about how it's calculated and how it applies to your subscription in our .
Starting today, CARTO supports through an Amazon Redshift connection leveraging the .
Additionally, we are giving all customers the option to t used to import files (instead of the default bucket provided by CARTO in cloud instances).
In this month's release of the Analytics Toolbox for BigQuery, we have published a new functionality within the that allows our users to perform the merchant universe matching analysis in order to derive insights on market penetration and to identify expansion opportunities. With this analysis, CPG players can match their current universe of merchants/customers against the total universe of all potential ones on a given market, in order to identify in which merchants their products are still not present.
This analysis is performed with two new procedures in the Analytics Toolbox: which performs a fuzzy match between two POI datasets based on location and name similarity, and that generates report-like tables summarizing market penetration insights.
Check the list of analyses available for each data warehouse and further documentation about each of them .
From today, users can start with the rest of users within their CARTO organization; who will then be able to open the shared workflow in view mode, and in the case of Editor users duplicate the workflow and edit the copied version as they wish.
From today, customers on Snowflake, Redshift and PostgreSQL have the possibility to use the public beta version of CARTO Workflows with data sources from their data warehouse connections. Note that CARTO Workflows is a new tool that enables users of all types and skill levels to harness the power of cloud data warehouses, , and advanced spatial analytics.
To learn more about this new development, please check our .
In the January 2023 release of the Analytics Toolbox for BigQuery, we have published a new and improved version of the module. This new version includes procedures to calculate origin-destination matrices and to compute isolines around a set of locations, both supporting multiple transportation modes (car, bike, and walk). These new functions run on top of (derived from OSM segments) that is available as a public subscription in the . Please note that these improvements imply breaking changes with the previous version of the routing module.
To learn more about these new procedures please check our . We have also published a to illustrate how to benefit from this module of the Analytics Toolbox.
In the January 2023 release of the Analytics Toolbox for BigQuery, we have launched in beta our new module. This feature offers a set of functions to operate with raster data natively in BigQuery, benefiting from the processing speeds and scalability of this data warehouse.
Alongside the raster module in the Analytics Toolbox, we have also made available our , built in collaboration with . This publicly available Python library works as a tool for loading and optimizing GIS raster data into cloud-based data warehouses.
In order to learn more about this new module please check our . We have also published an that illustrates how to use some of our functionality to combine raster and vector data to solve a spatial analysis.
Starting with this release, users that explore their CARTO Data Warehouse connection in will find two datasets (represented as folders) inside their organization data: private and shared.
The "shared" dataset will remain available to all the editor users in that organization. You can find all the documentation for this feature in the .
A data source (eg: adding a , using a ...)
A future location to save results (eg: , , ...)
We have improved the user experience for these cases by adding a search bar that works at every level of your data, adding a new view: the list view, and adding breadcrumbs to help you navigate your data. The list view is similar to the one used in (including the search bar and breadcrumbs) and will now make the experience more consistent across the CARTO platform. If you would rather focus on the hierarchy of your data, the tree view is still available on the top right.
With this new feature, point layers can be leveraging our Quadbin spatial index.
Today we are excited to announce that CARTO Workflows is now publicly available in beta with support for Google BigQuery and CARTO Data Warehouse. CARTO Workflows is a new tool that enables users of all types and skill levels to harness the power of cloud data warehouses, , and advanced spatial analytics.
CARTO Workflows provides a visual language to design and execute multi-step spatial analytics procedures, reducing the complexity and the high dependance on specialist knowledge to leverage the power of location intelligence. To learn more about this new development, please check our .
In the coming weeks we will add support to run CARTO Workflows on Snowflake, Redshift and PostgreSQL-based data warehouses, if you want to know more about that please contact us through our .
Hopefully, you'll have a better experience using this documentation. If you have any feedback about it, contact us through our . We'll keep working on documentation improvements during the following weeks.
We are thrilled to announce our new functions for line of sight and signal propagation analysis in the Analytics Toolbox for BigQuery. These new procedures, available in the module, enable network planners to run coverage analysis natively within BigQuery. With this functions users can now assess the geographical areas where current or potential new network's signal is available and evaluate its quality.
Path loss estimation of a signal as it propagates through an environment, with options for the and .
Learn more about these new features in our , and start testing them by following our step-by-step .
We are excited to announce the addition of two new space-time analyses available in the module of the Analytics Toolbox for BigQuery:
, to classify hotspots based on changes in their intensity over time, such as strengthening hotspots, declining hotspots, occasional hotspots, and more;
, to identify locations with similar temporal behaviors.
Learn more on how to perform these spatiotemporal analyses by exploring our tutorials for and .
Developers can also build their own large-scale heatmaps in their apps using CARTO + deck.gl, with the new heatmapTileLayer
(Experimental). Learn more from our and .
This allows users to import data directly from a public URL. It is compatible with BigQuery, Snowflake, Redshift, and PostgreSQL. By leveraging the CARTO Import API, this component ensures seamless data integration across different database systems.
The component supports workflows that run on a schedule or are executed via API, providing more robust and automated data management options.
The , which was limited to UI-based operations, will be deprecated. The new Import from URL component provides a more versatile and powerful alternative.
Users can now quickly from their computers directly into the workflow canvas. This feature supports drag-and-drop functionality, making it easier to integrate local files into your workflows.
Leverage all this new functionality by using the new component: A dedicated Workflows component that facilitates making requests to external APIs, providing enhanced versatility and extensibility. It uses the http_request
module from the CARTO Analytics Toolbox.
It also supports to embed logic directly into component settings using SQL operators combined with variable and column values.
Admin users at CARTO can now upload custom basemaps and tailor the basemap gallery options available to Editor users in Builder. Unleash your creativity and enjoy an enhanced map-making experience while maintaining a cohesive and consistent selection of basemaps throughout your organization. To learn more about how you can upload custom basemaps to the CARTO platform and the supported formats, check . For a step-by-step guide on custom basemaps, check out our in the Academy.
New Overview with a curated list of .
A simplified system to manage all your authentication methods.
A new list containing all your , for easy access.
Additionally, we've simplified the way that organizations decide the content in their section. Before, it was a mix of developer credentials and apps registered by the administrator. Now, administrators in CARTO are in full control of , including the visibility/sharing settings.
Developer credentials created before April 25th have been duplicated as applications to maintain the same visibility level as previously.
Import Cloud Optimized GeoTIFFs: Say goodbye to cumbersome raster data ingestion processes. With our latest enhancements, you can now effortlessly import Cloud Optimized GeoTIFFs to and via both the Import API and the . This provides a streamlined and efficient method for ingesting raster files into BigQuery and Snowflake, ensuring optimal storage efficiency and fast query access.
Workflow Components for Raster Analysis: Take your spatial analyses to the next level with our new Workflow components designed specifically for working with raster data sources. Whether you're looking to extract raster values or perform complex intersect and aggregate operations, our new components, including "" and "", provide you with the tools you need to unlock valuable insights from your raster datasets.
We've introduced several improvements to help Admins of organizations using SSO groups manage them more effectively. Admins can now view the composition of groups, search for specific users within them, and delete unused groups. Additionally, we've implemented a new method to synchronize only a subset of groups into CARTO. For more details, visit our article on .
today to explore the new Data Observatory catalog and unlock the full potential of your projects! Access more information about the Data Observatory in our .
Developers can manage their Named Sources manually via UI or programmatically via API. To get started with Named Sources, check the and the .
Learn more about in our documentation or read about it in our .
This functionality allows users to repurpose their maps effortlessly, even when the data source location in their data warehouse changes—eliminating the need to recreate maps from scratch.
Custom color palettes can be created from the Settings and are applied directly in CARTO Builder. For more information, see our article on .
We have introduced support for SQL Warehouses and Unity Catalog in .
Made Databricks connections available in across the platform, as well as geospatial applications developed with CARTO.
Enabled and for high-performance visualizations.
Workflows for Databricks leverages , and the to make geospatial analysis easier and more performant than ever for data scientists, engineers and analysts on Databricks. Being a cloud-native integration, CARTO pushes down all processing to Databricks, profiting from the massive computation capabilities.
Navigating large geospatial datasets is now faster with our upgraded , featuring search, highlight, and zoom capabilities.
Many times, a single basemap doesn't fully meet all of your mapping needs. Now, with the new in Builder, users can easily switch between different basemaps available in your organization. This feature allows you to tailor the visual context of your maps to specific use cases, enhancing the overall data exploration experience.
We’ve added a new styling property, _carto_point_density
, for point dynamic tiling sources, perfect for visualizing point density. You can use this property in Builder or your custom apps to style your points by radius, fill, or stroke color, making your maps more insightful and visually appealing. Learn more about it in our .
This is what we love about the new :
We're excited to see what you build! — To get started, head over to the or check the .
What is the new version of the CARTO platform?
Is the previous version of CARTO going to be deprecated?
How can I log into the legacy CARTO platform?
What happens to my current CARTO subscription? Will I have to pay extra to access the new platform?
Can I login to both versions of the CARTO platform with the same credentials?
Will I be forced to move all my data to the new version of the CARTO platform?
Is CARTO’s Student Package still available?
Can I setup a Single Sign-On integration in the new platform?
Where can I see my current quotas and usage?
In October 2021, we launched a fully revamped version of the CARTO platform. The new platform offers a complete cloud native experience, allowing to run CARTO on top of the leading cloud data warehouse platforms (e.g. Google BigQuery, AWS Redshift, Snowflake, etc.), eliminating ETL complexity and limits on scalability.
This new platform works completely independent of the previous version of CARTO; hence, requires a different set of user credentials to access. You can learn more about the new platform with our User Manual.
No, we don’t have plans to deprecate the previous version of our platform for our existing Enterprise customers, so you don’t need to worry about that now. However, it is important to note that any new product developments will happen only in the new version of CARTO.
You can continue to login to the previous version of CARTO from this page.
Free trials for the new CARTO platform are already available on our Sign up page. We also welcome all of our existing enterprise customers to contact us so we can provision them an account to the new platform for the reminder of their subscription term.
Although direct login to the legacy platform was removed from CARTO's website homepage in February 2024, you can continue to log into your account in the previous version of CARTO from this page.
If you are an existing CARTO Enterprise customer, we will give you an account to the new version of our platform without any additional cost for the reminder of your subscription term. Please get in touch with your CARTO representative or our Support team and we will provision you an enterprise account to the new platform according to your subscription plan.
No, the two platforms are completely independent, and hence they require their own set of credentials to login. In our login page users can select which version of CARTO they want to access. To access your old account, select “CARTO Dashboard”.
No, by default it is not. Both versions of the platform are completely independent. Get in touch with your CARTO representative or our Support team, and we can assist you to migrate your datasets across our two platform versions.
No, you won’t. If you are an existing CARTO customer you can enjoy both versions of the platform. If you are an existing enterprise customer, and you would like to make available in the new platform some of the tables you have in your existing account, please get in touch with your CARTO representative or our Support team, and we can assist you to migrate data across our two platform versions.
Yes, it is. Check our guide here and learn how to activate your CARTO Student account using the Github Student Developer Pack.
Yes, check all the details in our SSO documentation. To get started, just get in touch with your contact or with support@carto.com and we’ll guide you through the process. In the end, users in your organization will see your SSO integration as the only way to access your organization.
To understand the current plan limits (quotas) and how far are you from reaching them, there’s a section located in Workspace > Settings > Subscription > Quotas where you can check all this information at any time. Check Understanding your Organization Quotas for examples and more information.
Why should I migrate to the new version of the CARTO platform?
Is CARTO going to provide me assistance if I would like to migrate to the new platform?
What type of objects can be migrated between platforms?
Can I migrate my maps from the previous version to the new version of CARTO?
What information will you need to provide to receive assistance with the migration?
Do I need to provide an authorization for CARTO to work on my platform migration?
Will the platform migration tasks interfere with the standard service?
The new platform offers you a complete cloud native experience, and unparalleled geospatial analysis and visualization capabilities on top of the leading cloud data warehouse platforms (i.e. Google BigQuery, Snowflake, AWS Redshift and Databricks), eliminating the need of complex ETLs and the limits of scalability that our previous platform had.
In the new CARTO, we have implemented a new and more powerful version of CARTO Builder, further advanced our Data Observatory and Development tools, and created a new suite of geospatial analytics functions that can be run natively in the aforementioned cloud data warehouse platforms.
Our teams are currently laser focused on evolving both functionality and user experience to make the new CARTO the most powerful spatial analytics platform available in the market. Although we currently do not have any plans to deprecate the previous version, we recommend you to start your new projects in the new CARTO platform. CARTO will not be doing any further developments on the components of the old version of the platform, which will remain “as is”.
CARTO offers technical assistance for platform migrations to all our enterprise customers at no additional cost. If you have an Enterprise plan and you would like our assistance to migrate to the new version of our platform, please contact your CARTO representative or the Support team at support@carto.com.
We can only offer migration support between platform versions for data tables and your active Data Observatory subscriptions.
No. Unfortunately, since the technology stack between platforms is completely different, we cannot offer compatibility of maps built in the previous version of CARTO with our new technology. If you have TAM hours in your Support package, please ask your Customer Success Manager for an evaluation of the effort required to build your maps in the new platform.
In order for us to assist you with the migration to the new CARTO platform, we will need to receive the following information about your CARTO account in the previous version of the platform:
The name(s) of the CARTO organization(s) in the previous version of the platform that you want to migrate to the new version;
The name of the CARTO organization in the new platform to which you want to migrate your data to;
A list of the data tables to migrate;
An estimate on the size of the tables that you want to migrate;
Whether you have a cloud data warehouse from the supported ones (i.e. Google BigQuery, AWS Redshift, Snowflake, Databricks) or your own PostgreSQL database to which you want to migrate the data to use it with the new platform. If not, we will migrate your data to your instance in the CARTO Data Warehouse.
Yes. For us to be able to actively assist you with the platform migration, we will need you to send us a written authorization with the following text in order to give us the required permissions:
Authorization
“I request CARTO to create an additional Editor user in my CARTO organization account on the new platform version.
This Editor user will be only used by the CARTO team to migrate my data from the previous version of CARTO to the new platform (“Migration Services”).
I also grant CARTO access to my organization account {NAME OF THE ACCOUNT} in the previous platform version of CARTO as part of the Migration Services.
Once the Migration Services are completed, CARTO will hand over the tables created and will remove the additional Editor user created in my CARTO organization account.
CARTO’s team will only have access to the data in my CARTO accounts in order to perform the Migration Services.”
CARTO provides an instance of the CARTO Data Warehouse and monthly usage quotas with every subscription plan depending on the tier. Please get in touch with your CARTO representative to understand the service level associated with your subscription plan. You can read more information on this topic here.
Any task associated with the platform migration is not expected to interfere with your CARTO service. In any case, CARTO will agree with you on a timeframe to carry out the migration activities, so you are completely informed on when these will be performed and therefore reduce the chances of any interference with your own usage of your CARTO accounts.
My screen is stuck on the "Creating an Organization" page.
How can I join an existing organization on CARTO?
Can I be a member of multiple CARTO organizations?
Can I extend my free trial to longer than 14 days?
Can I start multiple free trials with CARTO?
If you have never signed up for CARTO before, this can normally be fixed by clearing your browser’s cache. If you have previously used CARTO or created another CARTO organization - including a free trial - please see the FAQs below.
A CARTO user with an admin role from your organization will be able to add you to their organization through the CARTO Workspace via Settings > Users and Groups.
If you have a previous or existing CARTO login (including free trials) which are not attached to this organization, you will not be able to immediately join. In this instance, you should contact your customer success manager or support@carto.com who can help you.
You cannot join multiple CARTO organizations with the same email address. If you have a requirement like this, you should contact your customer success manager or support@carto.com who can help you.
CARTO accounts are currently limited to one account per email, including historic accounts. If you have specific requirements around this, please contact your customer success manager or support@carto.com who can help you.
If you are attending a CARTO event where a free trial is a requirement, please contact the event organizers who will be able to coordinate this for you.
In most cases free trials are limited to 14 days. Please fill in our Request a demo form to discuss your requirements if you weren’t able to evaluate the platform within 14 days.
Free trials are limited to one per user. Please fill in our Request a demo form to discuss your requirements if you weren’t able to evaluate the platform with a single trial.
If you are attending a CARTO event where a free trial is a requirement, please contact the event organizers who will be able to coordinate this for you.
What methods can I use to create a map layer?
How can I run spatial analyses in Builder?
How does the export mechanism from Builder works?
To add a data source to a map as a new layer you can either:
Pick a table or tileset from one of your active connections to cloud data warehouses
Add data resulting from applying a custom SQL Query. You can also leverage the SQL functions available in CARTO’s Analytics Toolbox.
Importing data from a local or remote file. Right now we currently support GeoJSON, Shapefile (in a zip package), and CSV files. We’re working to support more formats in the future.
To run spatial analysis in Builder, you can use the SQL Editor, which is accessible when adding a data source as a custom query. The SQL Editor allows you to execute SQL commands directly in your cloud data warehouse (e.g., BigQuery, Snowflake, etc.), taking advantage of the full capabilities of the platform, including functions and operations available there. Additionally, you can leverage UDFs from the Analytics Toolbxo for enhanced spatial analysis. While the SQL Editor is ideal for performing simple analysis or utilizing SQL Parameters, for more complex or multi-step analysis, we recommend using Workflows. Workflows enable you to perform detailed, step-by-step analysis and save the results as a materialized table, which can then be used as a source in Builder. This approach provides greater flexibility and scalability for more advanced spatial analysis tasks.
The export mechanism in Builder leverages the built-in export functionality of the connected cloud data warehouse to handle and process data. This ensures efficient export of datasets, aligning with the performance optimization strategies of the underlying platform.
When using BigQuery specifically, the export process stores data in a Google Cloud Storage (GCS) bucket. For performance and scalability, BigQuery splits the exported data into multiple smaller files rather than a single large file. This behavior is expected and is a result of BigQuery’s internal strategies to parallelize the export jobs for optimal performance.
What cloud data warehouses can I use with CARTO?
What are the device/web browser requirements for CARTO?
When I connect to a data warehouse, do you copy or store any data?
What happens if I do not have any cloud data warehouse platform to connect?
Can I import geospatial files into CARTO’s new platform?
What are the Location Data Services (LDS) providers configured by default in a CARTO organization?
CARTO’s new platform is designed to give you a fully cloud native experience, allowing you to run CARTO on top of your leading cloud data warehouse platform of choice (i.e. Google BigQuery, Snowflake, AWS Redshift, Databricks, and any PostgreSQL-based data warehouse platform).
CARTO is designed to work in all modern browsers that meet the following criteria:
Complete support, including hardware acceleration, for WebGL2
A browser version not older than 2 years
This includes the latest stable versions of Google Chrome, Safari, Firefox, Microsoft Edge, and Opera, but other browsers using standard technology and meeting the criteria above should be compatible as well.
While CARTO should work in all browsers meeting the criteria above, the best performance and compatibility are expected with Chromium-based browsers.
Please note that the user's device must also have hardware that supports these features. A desktop device with a dedicated GPU and at least 8 GB of RAM is recommended.
No, your connection allow us to perform queries against your data on your behalf, and the results are either stored again in your data warehouse or rendered in the client, as visible maps. CARTO being fully cloud native means no storage needs, less security concerns and no need for data replication or complex ETL processing.
For users who do not have any cloud data warehouse platform to which they want to connect CARTO, we are offering cloud storage and computing resources in what we call the CARTO Data Warehouse. A CARTO Data Warehouse connection is offered by default with your CARTO subscription.
Yes, at the moment you can import both local or remote (via URL) Shapefiles, CSV or GeoJSON files. You have more details available in the corresponding section of our User Manual.
CARTO offers access to Location Data Services (LDS) such as geocoding, isolines and routing by leveraging the APIs of 3rd party service providers. Since December 2023, by default, CARTO uses TomTom APIs for geocoding and routing, and TravelTime for isolines.
CARTO retains the right to adjust the default configuration of these services at its sole discretion when deemed necessary. Other options to the default can be made available under special commercial and usage terms.
What frameworks and libraries can I use for developing custom apps with CARTO?
Are “CARTO for deck.gl” and “CARTO for React” compatible with the new version of the platform?
You can use any framework or visualization library because CARTO is based on industry-standards. If there is not a hard requirement, we recommend using deck.gl for visualization and CARTO for React for creating apps that extend the platform functionality.
Yes, you can use both tools with the previous and the new version of the platform.
CARTO does not currently offer an SDK for the development of mobile apps as a component of our cloud native platform. In order to develop mobile applications with geospatial data, we recommend using the relevant SDK of your cloud vendor, or from products such as Google Maps, Apple Maps, Mapbox or Maplibre.
Particularly for the visualization of small datasets with spatial data (< 30MB), all SDKs will support visualization of GeoJSON files (e.g. Google’s Maps SDK for Android), and CARTO’s Maps API can be the technology to serve them.
The Mobile SDK in the previous version of the CARTO platform will not be further developed, and we don’t recommend starting new projects with it.
New features and improvements introduced from April to June 2023
June 30th, 2023
New Workflows
We have just added a couple of new features in Workflows that are going to improve a lot the explainability of your multi-step analytical pipelines.
Rich notes supporting Markdown syntax.
Update nodes with more relevant and descriptive names.
June 30th, 2023
Improvement Workspace
Starting today, users with the ability to customize branding and appearance can also remove the CARTO brand and social icons from their public and embedded maps.
This is a setting that is applied to all maps created in the organization. Additionally, administrators can decide whether new users receive the generic CARTO onboarding materials, to further customize the experience for new users. Learn more about how to activate these customizations.
June 27th, 2023
New Builder
We are excited to introduce the latest enhancement to the Formula Widget in Builder, which allows users to create their own custom aggregation operations.
This new feature provides advanced capabilities for users to tailor calculations and derive precise insights from their data using SQL Expressions.
With custom aggregation operations, users have the flexibility to define calculations that align precisely with their unique analytical requirements. They can incorporate business-specific formulas and apply complex mathematical operations to single or multiple columns from their data source.
This level of customization empowers users to unlock valuable insights and perform advanced calculations that go beyond standard aggregations.
June 26th, 2023
New Workflows
While working with Workflows, in some occasions a component needs to be defined as a custom geography (point, line or polygon). This is currently the case with "Table from GeoJSON" but this tool will also be used in other components that might need a custom geospatial input.
We have developed a new tool, accessible through the "Draw features" button to define custom geographies as inputs for components.
This new tool come in quite handy in cases where one or more steps in an analysis have to be defined by a manual input, allowing faster prototyping a providing a much better user experience.
June 20th, 2023
Improvement Workspace
When importing geospatial data to a cloud data warehouse, one of the challenges is to select the correct data type for each of the columns in the file, also known as schema. And in most cases, CARTO automatically does the job for you, because we analyze a sample of the data and infer the data type from it.
For those cases where the automatic detection isn't exactly what you need, CARTO now allows you to manually defined the schema of the imported file, both through CARTO Workspace and Builder, and through our Imports API.
An example where this new feature is useful is when dealing with postal codes, that depending on the country could be automatically detected as numbers instead of strings — it doesn't make sense to calculate the average postal code.
To read more about how to select a custom schema in your imports, read our Importing data documentation.
June 14th, 2023
New Workflows
We have released a new batch of components in Workflows to keep increasing the possibilities and the value of this tool to enable the creation of data pipelines and spatial analyses for our users. The majority of components in this new batch are oriented towards providing more flexibility when manipulating and getting your data ready for the analysis. Here's the list of new components:
Multi-Col Formula: it computes new values based on a given expression and a set of fields to apply the expression to;
Multi-Row Formula: it creates a new table containing a new column computed using a multi-row formula based on one or several input columns;
Find Replace: it finds a string in one column of a table and replaces it with the specified value from another table;
Refactor columns: it refactors the columns in a table, allowing to change names and data types, and to select only certain columns from a table;
Transpose: it rotates table columns into rows;
Text to Columns: it adds new columns based on splitting the text string in a text column;
Unique: it separates unique rows and duplicated rows;
Row Number: it creates a new table with an additional column containing row numbers;
Quadbin To Parent: it adds a new column named quadbin_parent with the value of the parent quadbin at a specific resolution;
H3 To Parent: it adds a new column named h3_parent with the value of the parent h3 at a specific resolution;
H3 KRing: it returns the neighboring indexes in all directions under the K distance size;
H3 Distance: it computes the H3 grid distance between two H3 index column.
June 6th, 2023
New Analytics Toolbox
In the lds module of the last release of the Analytics Toolbox for BigQuery, Snowflake and Redshift we have now added the function to CREATE_ROUTES between given sets of origins and destinations (points) in a query, supporting different transportation modes and other advanced parameters. The function generates a new table with the columns of the input query plus a column with the resulting routes. Note that the routes are calculated by calling one of our external location data services providers. This functionality is also available from CARTO’s LDS API.
June 6th, 2023
New Analytics Toolbox
In the last release of the Analytics Toolbox for BigQuery we have available a new set of functions in order to perform space-time cluster analysis, for when data has both a spatial and a temporal component and you want to identify clusters looking at both dimensions at the same time (e.g. hotspots of demand for food delivery services in different periods of the day). Our implementation computes the space temporal Getis-Ord Gi* statistic for each area and timestamp according to the method described in this paper. This is supported now with two new functions in the statistics module of the toolbox, namely GETIS_ORD_SPACETIME_QUADBIN for quadbin indexes and GETIS_ORD_SPACETIME_H3 for H3 indexes.
May 16th, 2023
New Builder
Finding your current location on CARTO maps is finally possible. This feature is specially helpful when users require the map zoom in to their current position in a seamlessly manner to obtain insights from their surroundings.
This is how it works:
Click the Focus on User's Device Location button.
Enable Location Services on your browsers if required.
The map display zooms in to your current location and a blue icon indicates your position on the map.
May 8th, 2023
Improvement Workspace
The CARTO Data Warehouse is a connection that comes pre-created for every CARTO organization, and it's fully managed by CARTO.
Until this release, it wasn't possible to manage the data available to this connection other than what was already available through Builder and Workflows.
Now, all users can introduce a Google account that they'll use to access the console. Once inside, you can run any SQL query, copy and edit existing tables or use other built-in features to import and migrate your data. Read more on how to get access to the CARTO Data Warehouse console.
May 5th, 2023
New Analytics Toolbox
Users of our Analytics Toolbox for Snowflake have now access to a new module named “statistics” offering functions to compute statistical measures on top of your spatial data. In this last release we have added support for computing the Moran’s I spatial autocorrelation and the Getis-ord Gi* statistics used for the identification of hotspots based on an input feature.
May 5th, 2023
New Analytics Toolbox
In this last release of the Analytics Toolbox for PostgreSQL, we have added a new module named “h3” with a set of functions providing users support for operating over the H3 spatial index. H3 is a multi-resolution hexagonal global grid system with hierarchical indexing developed by Uber, offering important benefits when performing spatial analytics at scale. To learn more about Spatial Indexes and H3 in particular, please have a look at our Spatial Indexes 101 report and our documentation.
May 5th, 2023
New Analytics Toolbox
Some areas due to their intrinsic characteristics or the data available are not suitable for running the predictive models given the large differences within the data used when training those models (e.g. training a model on big cities and then running predictions in areas of low population density). We have added new functions in the statistics module to allow users to compute the Area of Applicability (AOA) of a BigQuery ML model. It generates a metric which tells the user where the results from a Machine Learning (ML) model can be trusted when the predictions are extrapolated outside the training space (i.e. where the estimated cross-validation performance holds).
In the case of our Analytics Toolbox for BigQuery, this functionality is particularly useful when working with our BUILD_REVENUE_MODEL and PREDICT_REVENUE_AVERAGE procedures of the retail module.
April 24th, 2023
New Builder
SQL Parameters are placeholders that can be used on any SQL Query data source in Builder.
Once defined, the actual value for the parameter can be set through a control UI in the right side panel’s 'Parameters' tab. This allows to manipulate the actual SQL Query through an UI, by both Editor and Viewer users.
Among many different use cases, some applications for this new feature are:
Create 'Text' or 'Dates' parameters.
Reduce the size of a data source before rendering the map.
Allow viewer users to define custom values in the data source in a controlled way.
Use the same parameter in one or more queries.
Filter a dataset before aggregating it to a spatial index grid (H3 or Quadbin).
Learn more about how to set up and use SQL Parameters in your maps here.
April 21st, 2023
New Workspace
Developers looking to create geospatial applications at scale usually face an authentication challenge: how to build the application so that data is accessed in a granular and secure way. And there are different solutions depending on your needs: from static API Access Tokens for simple, public applications to dynamic authentication using the CARTO login (with or without Single Sign-On).
Today we're making the creation and management of API Access Tokens much simpler, with a complete user interface to create, edit and delete tokens.
API Access Tokens are now the recommended method to start working with CARTO for deck.gl and the CARTO APIs, and we've updated the documentation and API reference accordingly.
Learn here how to create and manage your API Access Tokens.
April 13th, 2023
New Workspace
CARTO is the ideal solution for geospatial visualization and analysis of large scale datasets, due to the scalability of the cloud data warehouses (eg: BigQuery, Snowflake, Redshift, Databricks...). However, dealing with such large datasets requires special attention into performance and optimizations.
Now, whenever we detect that one of your tables could perform better according to our performance considerations, we'll show a warning in Data Explorer and Builder, and you'll be able to take action immediately.
In just a few clicks, you'll overwrite or generate an optimized copy of your data, that will perform faster and save computing costs.
To understand in detail how these optimizations work, head to the Optimizing your data guide.
April 10th, 2023
New Workspace
We are adding two new features for admins to manage new users more easily and predictably:
Default role for new users: Admins can now choose which role should be applied to new users, and by default it will be set to "viewers" following the least-privilege principle. Find more information about roles in CARTO and about this setting.
SSO Just-in-time provisioning: Admins that have integrated their own SSO login can now decide whether new users will get additional questions when onboarding or not. If it's enabled, we'll just provision their user as soon as they login, without any needed step. This new setting has been included in the documentation about SSO at CARTO.
April 6th, 2023
New Builder
CARTO Builder now supports adding labels to point layers loaded via tiles, with a set of improved features:
Support for primary and secondary label on each point.
Now using a better typography, increasing readability.
Collision control: Now labels are displayed in a way that they don’t collision with each other, adapting dynamically on each zoom level.
Custom colors for the font and outline, allowing much better customization capabilities.
April 3rd, 2023
New Analytics Toolbox
In this month's release of the Analytics Toolbox for BigQuery, we have published a new functionality that consists of a set of procedures within the statistics module to enable users to create spatial scores (also known as composite indicators or indexes) derived from a combination of different features. We have included 3 different procedures:
CREATE_SPATIAL_COMPOSITE_SUPERVISED: to compute a spatial composite score as the residuals of a regression model which is used to detect areas of under- and over-prediction.
CREATE_SPATIAL_COMPOSITE_UNSUPERVISED: to perform an aggregation of individual variables, scaled and weighted accordingly, into a spatial composite score.
CRONBACH_ALPHA_COEFFICIENT: to measure the internal consistency of the variables used to derive the spatial composite score.
New features and improvements introduced from July to September 2023
September 30th, 2023
Improvement Builder
We have upgraded our Time Series Widget in Builder to boost your temporal data analysis experience. These enhancements bring a new level of customization to your data exploration, offering:
Enhanced temporal precision: Our upgraded Time Series Widget empowers you with precise control over temporal data aggregation. You can now extract insights using custom time intervals and enjoy greater granularity, resulting in more accurate analyses.
Analysis of multiple time series: Unlock the ability to analyze multiple time series simultaneously within the widget, enabling seamless concurrent analysis over time.
September 30th, 2023
New Workflows
We have released a new set of components in Workflows for Data Enrichment:
Each of them allows to enrich different types of geospatial data, and all of them allow using both a Data Observatory subscription or a custom table as source for the enrichment data.
Additionally, we have released a new component for advanced Statistics and for Retail Analytics, initially supported in Google BigQuery:
Commercial Hotspots
These additions to Workflows will make it a lot easier to leverage data enrichment and advanced statistical capabilities in CARTO, integrating these complex processes as just another step in a workflow.
September 7th, 2023
Improvement CARTO for Developers
Developers building applications with CARTO can now leverage their existing Single Sign-On (SSO) integration enabled in their organization to authenticate users.
Given the right setup, these applications will now be able to manage existing users and also first-time users coming from the SSO Identity Provider (IdP) that didn't exist previously in CARTO. The experience for these new users is seamless, without any action or step required. This is an extension of the Just-in-time provisioning setting available in CARTO Workspace.
The changes needed to fully leverage SSO and Just-in-time provisioning are covered:
For custom applications: in a new Build a private application using SSO guide.
For new and existing CARTO for React applications: in the Authentication guide.
September 5th, 2023
New Workflows
We have just made Data Observatory subscriptions available in the Data Sources panel in Workflows.
This will make premium and public datasets a lot easier to work with: just drag and drop your available samples or subscriptions to the canvas and start building your workflow.
With this new addition to Workflows, the largest catalog of curated geospatial datasets is readily available to be integrated with your cloud native analytical pipelines with just a few clicks. Check this new feature documentation here.
September 4th, 2023
New Builder
We're delighted to announce the next level of map description functionality in CARTO Builder: Richer map descriptions with support for Markdown. This upgrade takes our previous map description feature to a whole new level.
With the new richer map descriptions, you're not just adding text; you're crafting a more engaging user experience. The support for Markdown syntax allows you to include various text formats, headers, links, images, and even bullet-point lists, elevating the user's understanding and interaction with your map. To learn how to add Richer Map Descriptions to your maps, click here.
August 9th, 2023
New Builder
We're thrilled to unveil our newest addition to CARTO Builder - the Numeric SQL Parameter, expanding our portfolio of supported SQL parameter types. This innovative feature offers users enhanced interaction with numerical data within Builder.
Leveraging the Numeric SQL parameter, users can seamlessly retrieve single or pair numeric values from a Control UI to update the underlying data sources. It's an excellent option for those requiring to filter data by specific numeric ranges or adjusting analytical outputs based on numerical inputs.
Learn more about how to set up and use SQL Parameters in your maps here.
July 24th, 2023
New Workspace
We have added the possibility to control the visibility of Applications in CARTO through the usual sharing options (private, entire organization...), including the ability to share an application only with specific groups of users.
This is especially interesting to customize what applications are shown for each user in the CARTO Workspace depending on the groups that they belong to, and also to start developing applications privately, without the app shortcut being shown to other users. You will find more information for these use cases and other details in the Managing Applications section in this documentation.
July 20th, 2023
Improvement Workspace
Administrators in CARTO now have the possibility to automatically assign a role to their users based on the groups that they belong to. To do so, just enable this feature and map each group to a user role. For example, you can map the group acme_data_analysts
to get the editor role in CARTO, and new users belonging to that group will automatically get the editor role as well.
This is a powerful approach to quickly onboard dozens or hundreds of users into CARTO while maintaining effortless and enterprise-grade controls over the privileges of each user. Learn more about mapping groups to user roles.
July 13th, 2023
New Workspace
Previously, whenever you created a BigQuery connection using OAuth ("Sign in with Google") it had to remain private, to prevent other users from impersonating your personal credentials.
With the addition of the new viewer credentials mode when sharing connections, we're unlocking several benefits for organizations using BigQuery:
Now you can collaborate in maps using a shared BigQuery OAuth connection
Instead of creating one connection per user, you can create just one connection and share it with everyone, with fewer management issues.
By requiring viewer credentials, you can leverage row-level security and other policies set in your data warehouse.
July 6th, 2023
New Builder We are thrilled to introduce the enhanced Search Location Bar, formerly known as the Address Search Bar. This feature now includes the ability to search for locations using coordinates. Simply input latitude and longitude values, and instantly visualize the corresponding location on the map.
Whether you're exploring remote areas, analyzing specific points of interest, or seeking valuable insights, our coordinate search feature empowers you to navigate with precision and seamlessly uncover new possibilities.
New features and improvements introduced from October to December 2022
December 29, 2022
New Builder
Customers relying on PostgreSQL and PostGIS for their geospatial data will now be able to create and execute analyses directly from Builder.
These analyses are created as dynamically generated SQL queries that are pushed down to a PostgreSQL database through a CARTO connection.
The result can be visualized, used as input for another step of the analysis, or persisted into a new table.
December 27, 2022
Improvement Analytics Toolbox
In the last release of the Analytics Toolbox for BigQuery, Snowflake and Redshift we have added the possibility to configure more options as parameters when executing the functions to CREATE_ISOLINES. These new options, which depend on the LDS service provider, allow the user to configure more transportation modes such as truck or bike, the possibility of specifying departure or arrival times allowing the creation of reverse isolines, and other options like different routing modes. Additionally, we have added new confidence/relevance metadata to the results of the geocoding function GEOCODE_TABLE.
December 27, 2022
Improvement Workspace
CARTO Workspace now supports importing geospatial files through a PostgreSQL connection leveraging CARTO Import API.
With this new functionality, CARTO users working with a PostgreSQL database will be able to get their geospatial data ready for advanced analysis and visualization in Builder and CARTO for deck.gl.
December 27, 2022
Beta Analytics Toolbox
We have released within the cpg module of the Analytics Toolbox for BigQuery a new function named FIND_SIMILAR_LOCATIONS that allows users to identify which locations (e.g. merchants, stores) are more similar to a chosen location (e.g. top performant) based on the characteristics of their surrounding areas (or trade areas), which can be configured to be based on demographic features, environmental, nearby points of interest, footfall, etc. In this example we illustrate how to use this new analysis function to solve the aforementioned use-case.
December 5, 2022
Improvement Workspace
We’ve improved some scenarios for users who created a Google BigQuery connection:
Now CARTO should behave smoothly when your credentials (Service Account or OAuth) have access to more than 2000 projects. You should be able to select any of them as your billing project, and the Data Explorer will also let you explore all of them in a quick search.
Now it’s possible to repair Google OAuth connections. Before, if you connected using “Sign in with Google” (often referred as OAuth), this connection could break after this authorization is revoked. This could happen automatically after changing your password, for example. Using the new re-connect flow will authorize CARTO again in the same connection, so all your maps will continue working as usual.
November 29, 2022
Beta Analytics Toolbox
Today we are making available the option for Admin users to install the Analytics Toolbox in their Snowflake accounts with a simplified process assisted by the CARTO UI.
From the Settings section of the CARTO Workspace users can now install, update and uninstall the Analytics Toolbox for Snowflake without external support. All details for setting up your Snowflake resources and to carry out the installation process can be found in our documentation.
November 18, 2022
New Developer Tools
A new version of CARTO for React has been released with the following main highlights:
Support for parameterized queries. Now, a user can define queries that allow for external parameters to be injected into the query and create more powerful dynamic queries without having to modify the SQL; this, will result in filtering being applied from the backend side to the sources and will be reflected in layers and widgets. For more information, a guide has been included in our documentation and can be accessed here.
Several bug fixes.
November 15, 2022
New Applications
Batch simulation of candidate locations is now possible in the Site Selection application. Instead of simulating locations one by one, users can now use a CSV template to upload in bulk the location details of their candidates. They can subsequently edit and remove their locations in the application as they see fit before running a batch simulation.
This feature enables users to process in bulk lists of candidate locations often provided by separate research teams, rather than one by one.
November 15, 2022
New Applications
Users can now explore the impact of the revenue prediction model features directly through the Site Selection application.
For each simulated location and associated predicted revenue, the widget showcases the magnitude of the impact of the features included in the model (i.e. population, mobility, POIs, etc.), as well as whether they contribute to predictions positively or negatively.
November 15, 2022
New Builder
Logarithmic scales are now available as a data classification option in Builder.
While they’re available for all kind of sources, a logarithmic scales based on powers of 10
will be the default option for aggregated data sources.
This new addition will make it easier to create better cartography when working with spatial indexes, as well as a handy additional method of classification for other types of maps.
November 7, 2022
Improvement Workspace
Following the release of geocoding and isolines for Google BigQuery, and the new layout for the Settings, we’re adding new trackers for quotas in Workspace so users can understand and predict their consumption.
We added a new “CARTO for Developers” section, including:
Existing quota: Applications, for applications created using Workspace
A new quota: Tokens, for tokens generated using the Tokens API
We also added a new “Location Data Services” section, including tracking for Geocoding and Isolines operations. These quotas are reset every month, and each unit represents a row processed.
Finally, the “Connections” quota was removed, and will be gradually removed so users can create as many connections as needed without any warnings.
November 7, 2022
New Builder
Users working with Spatial Indexes data (Quadbin or H3) in Builder have a couple of new additions that will help them create better and more insightful visualizations:
The possibility of adjusting the aggregation resolution for a finer control over the aggregation.
Aggregation operations for categories: MODE
to get the most frequent category in the aggregated cells; ANY VALUE
to get any of the aggregated categories.
October 27, 2022
New Analytics Toolbox
Starting today, our Databricks users have the possibility to generate spatial index tilesets based on H3 natively in Databricks.
The tiler is a module of our advanced Analytics Toolbox for Databricks that allows to process and visualize very large spatial datasets stored in Databricks. If you are interested in it, please contact with support@carto.com to receive more information about it.
October 25, 2022
New Analytics Toolbox
Users can now enrich their data tables in Amazon Redshift with features from both their Data Observatory subscriptions and from their other 1st party data tables.
Procedures for Data Enrichment are now included in the Data module of the Analytics Toolbox for Redshift, specific for working with point data, polygons or spatial indexes. Please check out our documentation to find all the details and examples.
October 18, 2022
Improvement Workspace
Continuing our efforts to improve our sign up and login processes, we’re now launching a new experience. Users should be able to join CARTO in a more smooth way with these new additions:
A screen now will offer users the chance to create a new organization or join an existing one if there are users from the same domain.
The list of organizations to join now has details about the users, the plan and a search bar to find the desired organization.
When you request to join an organization you can now cancel that request (if it was undesired or the admin is unresponsive).
When following an invitation the signup form will now be already pre-filled.
The process to join an organization is now simpler with less steps.
Multiple bug fixes and minor improvements.
October 18, 2022
Beta Analytics Toolbox
CARTO now provides a set of Python packages to allow data scientists to work with our platform from within Python notebooks. These packages allow users to work with geospatial data in a fully cloud native way without having to leave their Python environment, and taking advantage of all the potential that Analytics Toolbox provides to execute advanced spatial analytics in Spatial SQL natively within the leading cloud data warehouse platforms.
October 18, 2022
Private Beta Builder
Update January 13th, 2023: this feature is now in General Availability and it's available to all CARTO cloud users. Read all details here.
Collaborating on CARTO maps is finally possible, in asynchronous mode. This is helpful in setups such as production-ready maps, where the original owner might be out of the office; or in situations where the data, analysis, and cartography are each accomplished by different users.
October 13, 2022
New Workspace
Starting today, it is available to all users the possibility of generating both Spatial Index and Point Aggregation tilesets on their own data tables directly from the Data Explorer UI. This feature complements the existing workflow, with the possibility of generating tilesets of large datasets based on spatial index (H3 and quadbin) and points, by defining aggregations on the interested features. The platform detects automatically if the table is based on spatial indexes or points and provides the new options in the “Create Tileset” wizard.
October 11, 2022
New Builder
We have just added a new exciting component to Builder. The new Date Filter allows you to reduce the size of a data source by selecting a specific time range from a date or timestamp column in your data.
It is available for dynamically tiled data sources, which basically means tables bigger than 30MB and Custom SQL queries. Find more information about data source sizes here.
When dealing with temporal series it is very common to find overlapping points, repeated geometries or spatial indexes… which make the analysis and visualization of the data cumbersome and difficult to visualize. This new component lets the user select a specific time range to filter their data, making all these problems easier to work around.
This new filter actually pushes down a SQL filter, which reduces the amount of data processed and transferred, while the Time-Series widget allows filtering the data when it has already been loaded in the browser. They can play very well together, using the filter to pre-select a time range to work with, and the Time-Series widget for finer client-side filtering, visualizing the series, animations, etc
As an Editor, you can decide whether or not to include the Date selector in the public map. This allows deeper data exploration for viewer and public users.
October 6, 2022
Beta Analytics Toolbox
We have released in beta a new domain-specific module in the Analytics Toolbox for BigQuery to solve advanced geospatial analysis for the CPG / FMCG sector, starting with customer segmentation. We now offer a set of procedures that allow users in that industry to solve this use-case end-to-end, from the generation of trade areas to running multiple segmentation scenarios of merchants based on a customisable set of spatial features. In this recent blogpost we showcase how to use these analytical routines with a specific example.
My workflow is producing the error message “No value assigned.” What could be causing this?
This may be because the data warehouse connection associated with the workflow does not have the required permissions to run Workflows in the data warehouse, such as the permission of creating schemas in the Workflows temp. location (configured in the advanced options of the connection card). Please choose or create another connection with data owner permissions or modify the permissions in the current connection and try again. If the issue persists, please contact our support team at support@carto.com.
In order to function, CARTO Workflows creates a temporal dataset in BigQuery named workflows_temp
in where to store temporary objects needed to fully execute a workflow. In BigQuery, we create such dataset in the default region of the GCP project associated to the BigQuery connection. If you then want to include in a workflow data sources that are stored in another region different to the "default" one of your GCP project, then you need to create a new workflows_temp
dataset in that other region and specify its location in the Advanced options of your BigQuery connection.
In order to guarantee a successful execution of a workflow via a Snowflake connection, please make sure that in the settings of the connection you have specified the database of your Snowflake account with which you want to work via that specific connection. This field is now required by CARTO Workflows. Note that you can edit an existing connection at any time.
Usually this error occurs when either the input data sources have not finished loading, or the workflow has not had time to fully initialize before you’ve run it. This means that your later components have not had a chance to work out which values it will be receiving from the previous components.
This can usually be fixed by re-running the workflow.
New features and improvements introduced from July to September 2024
October 4th, 2024
New Accounts
We've introduced a new toggle in the settings that allows Admins to enforce SSO within their organization. When enabled, every single user in that organization will have to authenticate using Single Sign-On, regaldless of their role. Users that try to authenticate with other mechanisms, such as User/Password and Google Account will not be allowed to log in.
For more details, check out our section on SSO.
September 26th, 2024
New Workflows
We’re pleased to introduce several updates to Workflows designed to improve both functionality and security:
Export Data from Any Node
A new Export button is now available on the Data tab for every executed node in your workflow canvas, allowing you to export data directly. This asynchronous export process can be tracked via the Activity Panel, similar to how exports are managed in Builder.
Enhanced Security for Enterprise-Ready Components
In line with our ongoing platform-wide security initiative, we've implemented the following updates:
Send by Email now works without requiring attached data, offering more flexibility in workflow automation.
You can now specify a custom bucket location when using the Send by Email component, giving you control over where your data is sent.
Export to Bucket no longer uses public buckets. Users are now required to specify their own bucket locations, ensuring more secure data management.
These updates make Workflows an even more powerful tool for enterprise users while maintaining a focus on security and ease of use.
September 11th, 2024
New Workspace
Users can now require viewer credentials for their Snowflake OAuth connections. When this option is enabled, anyone accessing data through the connection — whether they're consuming maps, running workflows, or using the data explorer — will need to authenticate with their own credentials.
This ensures that security policies set in the database, such as Row-Level Security, are enforced. For more details, visit our section on Sharing connections.
August 30th, 2024
New Workspace
As organizations roll out CARTO to different teams and larger groups of users, it becomes increasingly important for administrators to understand and monitor how their organization is using CARTO, and this is now easy, powerful and flexible thanks to the new CARTO Activity Data feature
Administrator can now easily export (manually or programmatically via API) a comprehensive data collection of everything that happened within their CARTO organization.
The new Activity Data can be then analyzed to deeply understand things like:
Basic engagement indicators: weekly active users, workflows run per week...
Most used features: most used workflow components...
Quota consumption: who is consuming more quota and why
And many more insights about your CARTO organization
Want to get started? Head over to the CARTO Activity Data documentation. Make sure to also check the full Activity Data Reference, as well as the Examples where we share practical guides and SQL queries on how to analyze this data.
31st July, 2024
Improvement Workspace
We've introduced an improved flow for transferring user assets (maps, workflows, connections, etc) when deleting a user or when downgrading an Editor/Admin to Viewer. From now on, Admins will be able to select the specific user that will receive the assets.
For more information, check our documentation on Deleting users and Managing user roles.
31st July, 2024
New Builder
We've introduced a new functionality in Builder to dynamically visualize your point data as H3 aggregations, helping you gain deeper insights and uncover trends more effectively.
By aggregating point data, you can:
Simplify Complex Data: Aggregate large volumes of point data into meaningful patterns and trends, making it easier to interpret and analyze.
Enhance Performance: Improve rendering times and performance, especially with large datasets, by reducing the number of individual points displayed.
Identify Hotspots: Quickly identify areas of high density or activity, helping you make data-driven decisions.
Visual Clarity: Reduce visual clutter by grouping nearby points, providing a clearer and more informative map visualization.
Simply select this new visualization type and enjoy the benefits of aggregated data visualization, all with exceptional performance thanks to CARTO's native support for spatial indexes.
29th July, 2024
New Builder
We are excited to introduce an improved layer panel in Builder for configuring your layers. This update significantly enhances the UI and UX of this panel, making the experience of creating visualizations in Builder even more enjoyable and efficient.
The redesigned panel features a cleaner layout and includes a new 'Data' section at the top. In this new section, you can define the spatial definition of the source linked to your layer. This is especially useful if your source contains multiple spatial columns or if Builder cannot recognize the spatial column by default.
Learn more about the spatial definition of your sources here. Also, explore our updated documentation section for layers to get the most out of this update.
29th July, 2024
New Workflows
We are pleased to announce the integration of our collection of Workflow templates directly within the CARTO Workspace. This feature aims to streamline your workflow creation process, making it faster and more efficient to access and utilize pre-built templates.
Integrated Collection: Access a wide range of workflow templates hosted on the CARTO Academy website, now readily available in the CARTO Workspace.
Simplified Process: Users no longer need to visit the Academy site to download and import templates. The new feature allows you to easily recreate templates by selecting ‘New Workflow > From template’ within the Workspace.
Enhanced Usability: This integration ensures that all available templates can be accessed with just a few clicks, promoting best practices and facilitating quicker setup of workflows.
This feature is designed to ease the learning curve by providing immediate access to valuable workflow templates that illustrate both building blocks for common geospatial analytics and more complex use cases, like industry-specific analysis for Telco, Insurance, Retail and CPG, Out of Home advertising, etc
19th July, 2024
New Builder
We are excited to introduce the zoom to layer functionality in Builder, which allows you to easily zoom to the layer extent, providing an immediate view of your dataset. When layers are filtered by widgets or parameters, the zoom focuses on the filtered data, ensuring you see exactly what's relevant.
Additionally, we have incorporated a "Show only this/Show all layers" feature, allowing you to quickly toggle all layers on and off with a single action, especially useful for maps including multiple layers.
Whether you're exploring vast datasets or gathering insights on geospatially distributed features, these new features will ensure a better exploration experience!
Learn more about this feature in our documentation.
15th July, 2024
New Workflows
We are excited to introduce a powerful new set of components in Workflows that significantly enhance your geospatial data processing capabilities. These components are designed to facilitate the creation of various types of tilesets, allowing for efficient visualization and analysis of large spatial datasets. Here are the key features:
Create Vector Tileset
Generate vector tilesets from point, line, or polygon tables, enabling smooth and interactive map experiences.
Create Point Aggregation Tileset
Aggregate point data along with their properties into tilesets, ideal for visualizing dense point data on maps.
Create Quadbin Aggregation Tileset
Generate tilesets by aggregating quadbin indices, providing a fast and scalable way to manage spatial hierarchies and visualize large datasets.
Create H3 Aggregation Tileset
Utilize H3 hexagonal indexing to create aggregated tilesets, perfect for detailed spatial analysis and representation.
These new components enable you to transform your spatial data into highly efficient and scalable tilesets, which can be seamlessly integrated into your mapping applications. For more detailed information on how to use these components, visit our documentation.
New features and improvements introduced from July to September 2022
September 19, 2022
New Builder
Now users can include custom icons as markers for point data in Builder maps.This feature includes two differents ways of selecting an icon:
Using one from our preset collection; based on the well-known Maki icon library, which is designed for cartography purposes;
Uploading a custom icon in .png or .svg formats.
Different markers can also be defined by the values of a categorical column, and can even be rotated based on a numeric value; which enables different use-cases such as rotating an arrow based on azimuth for telecommunication antennas or the wind direction in weather maps.
September 16, 2022
Improvement APIs
We have released a few changes in how we cache API requests in the CDN that will produce a significant improvement in the overall performance of the platform; specifically applying to Builder maps and applications developed using our APIs. Learn more about such changes in our documentation for developers at api-docs.carto.com; each end-point in Maps API and SQL API now contains a reference about our caching strategies.
In Builder, users have new a couple of new features:
“Refresh data source”: to make sure users get non-cached versions of the data. Note that with this option your map will be skipping the CDN and getting the data each time from your data warehouse.
“Refresh data source every X”: to allow the user to control the update frequency of the data displayed on public maps.
September 15, 2022
Improvement Workspace
We have improved the layout in the Settings section in the CARTO Workspace; providing a better way to organize different areas by topic and providing a smoother interface for explaining the different Settings options for your CARTO account.
September 13, 2022
Improvement APIs
A couple important fixes have been implemented to our Dynamic Tiling strategies. Dynamic Tiling is the technology CARTO has developed to dynamically generate tiles for medium sized dataset and layers loaded as SQL Queries from your cloud data warehouse.
When working with points, many times widgets were not showing data due to our “visual aggregation” strategy when points were very close to each other. We have now removed this type of aggregation, and we are only applying a limit of 200k points per tile to prevent performance issues. If now you encounter widgets not showing data, you just need to zoom in to reduce the number of points per tile.
With our previous strategy some polygons or lines that were falling in the intersection of multiple tiles were splitted for visualization purposes, which was making the same data point count multiple times in widgets. We have solved this problem by asking the user to identify a unique id property for the data source at the time of creating widgets.
September 7, 2022
Improvement Workspace
As part of an active taskforce to improve our sign up and login processes, we have now released an improved interface for Admin users to manage invitations to join the CARTO organization and to manage user requests to join it.
August 26, 2022
New Analytics Toolbox - Builder - Workspace
We are very excited to announce that users of Google BigQuery can now geocode their tables with address data and create trade areas around locations based on drive/walk time isolines natively from their data warehouse. These procedures call external location data service providers such as TomTom, HERE and Mapbox. Please check the SQL Reference of our Analytics Toolbox for BigQuery for more details, and also refer to our examples on how to geocode your data and create isolines.
Note that these functionalities are also enabled from the Data Explorer and Builder tools.
August 26, 2022
New Analytics Toolbox
Users of AWS Redshift can now access a new set of geostatistics functions to expand the spatial capabilities of their data warehouse with CARTO’s Analytics Toolbox. We have released Getis-ord Gi*, Moran’s I and p-value methods that can run natively with your data hosted in Redshift. Learn more about these analytical functions in our product documentation.
August 5, 2022
New Builder
From today, users of Builder can add a new type of widgets to their interactive maps. The Range widget allows you to filter data based on precise numeric ranges.
August 4, 2022
New Analytics Toolbox
Retailers working with Google BigQuery and CARTO can now analyze the potential cannibalization cased by a set of new stores into their existing networks, based on the overlap of the different trade areas in terms of geographic area but also in terms of any other spatial feature that the user wants to use in the analysis (e.g. population, number of households). Check out our documentation and this example to learn more about how to run this analysis with our Analytics Toolbox for BigQuery.
July 28, 2022
Improvement Builder
Users can now rename the data sources added to a Builder map; although seemingly a small product addition, this new feature brings a big improvement in terms of user experience for our users.
July 15, 2022
Improvement Workspace
We have introduced a new design in the Data Explorer that brings a good amount of improvements for our users: it allows to search and sort data objects within connections, provides pagination and infinite scrolling for connections with access to thousands of tables, facilitates access to Data Observatory subscriptions, includes shortcuts for creating new connections and importing data, etc.
July 15, 2022
New Analytics Toolbox
Postgresql users can now generate tilesets based on spatial index data (i.e. H3, Quadbin) natively in their databases. This new functionality from our Analytics Toolbox for Postgresql enables our users to build high performance data visualizations from very large datasets. Check out this example to learn more about how to use this feature.
July 12, 2022
New Developer Tools
We are excited to announce a new release of our CARTO for React library, packed with awesome new features to extend the CARTO platform and provide more capabilities for building custom solutions:
We have added support for spatial indexes, so now you can visualize layers and add widgets when you are working with datasets using H3 and Quadbin indexes, in addition to traditional geometries. This is specially useful when you are dealing with large datasets.
We have support now for dynamic tiling. By default the CartoLayer will work with dynamic tiles and the widgets have been updated to work with them.
Widgets now have two different modes: viewport and global.
The GeocoderWidget now is compatible with the new LDS API.
We have a new BarWidget to display categorical/qualitative data using vertical bars.
July 8, 2022
New Workspace
You can now synchronize the user groups coming from your Single Sign-On (SSO) directory (e.g. SAML, LDAP, etc.) with CARTO. Thanks to this new feature users can now share maps and connections with those inherited groups. You can access full details in our documentation on sharing with groups and group management.
July 7, 2022
New Builder
We have released a new feature for pop-up windows in Builder maps. Now users can customize their pop-ups using HTML and a templating system that allows accessing feature’s properties. This kind of flexibility allows users to add dynamic content to their maps, such as: Google Street View images, custom links based on data properties, images, logos, GIFs, etc. Check out this blogpost to see some examples of this feature in action.
July 1, 2022
New Developer Tools
We are really happy to announce a new release of “CARTO for deck.gl” to allow developers to build even more awesome apps and map visualizations with the CARTO platform. This new release comes from:
Support in the CartoLayer for datasets using spatial indexes such as H3 and Quadbin. You can now build visualizations with very large datasets without the need of geometry data at an incredible performance and reduced costs.
You can now leverage our Dynamic Tiling system with SQL Queries, providing great scaling capabilities to your maps.
deck.gl code base is now migrated to TypeScript. This improves the robustness and maintainability of TypeScript apps using deck.gl as well as the deck.gl codebase itself.
CARTO’s Analytics Toolbox is a set of UDFs and Store Procedures to unlock Spatial Analytics directly on top of your cloud data warehouse platform. It is organized in a set of modules based on the functionality they offer.
You can use the functions in the Analytics Toolbox via CARTO Builder, SQL Notebooks, and directly in the console of your cloud data warehouse platform.
To learn how to get access to the toolbox please visit the Documentation page for the:
For BigQuery connections in US and EU regions, we recommend to use the Analytics Toolbox we have enabled in US multi-region (project name: carto-un) and EU multi-region (project name: carto-un-eu)
Note that this also applies if you want to leverage the Analytics Toolbox from your CARTO Data Warehouse connection.
No, premium data subscriptions are only available for Enterprise plans. For Trial and Student plans, you will only have access to data samples or public data products from the Data Observatory.
Individual plans do not get access to the Data Observatory. If you need to use it, you would need to upgrade to a Starter plan, or get in touch with sales@carto.com
Premium subscriptions are offered on a Data-as-a-Service model based on yearly or multi-yearly licenses. Once the subscription expires and it is not renewed, the user needs to stop using and delete the associated datasets from the account.
It will depend on the data provider and the type of license you have purchased for your premium data subscription. Some data providers offer different types of licenses if their data is going to be used only within a CARTO application or exported into other technologies.
Why do you need this?
🎓 You are a student
🏫 You are an educator or academic institution
We routinely hear from students, teachers, professors, and university administrators that they’d love to use CARTO in the classroom. Here is how schools and individual students may make use of CARTO:
Individual Student Accounts: free CARTO accounts via GitHub Student Developer Pack
Educator Accounts: free CARTO accounts by request
Enterprise Accounts for Education: discounts and grants on a case by case basis
To verify that only eligible students are accessing CARTO, we take advantage of Github’s verification system. This means you will need to go through their channels to ensure you receive the proper student account:
Step 1: Sign up for Github
✅ yourname@university.edu
❌ yourname@gmail.com
Here’s a video-tutorial with all the steps:
Step 2: Apply for the Github Education Pack
To be eligible, you must
Be a student aged 13+ and enrolled in a degree or diploma granting course of study
Verify who you are with one of the following:
a school-issued email address
provide a valid student identification card
other official proof of enrollment
Step 3: Wait for verification and confirm
Once you apply, Github will need to verify you are, in fact, a student. This could take from 1 hour to several days. Please be patient and wait for your official verification, it is important for the process.
Upon verification, you will receive an email from Github that you have access to the Education Pack
Step 4: Claim your CARTO student account
This process will connect your GitHub account. Remember you should that URL for login too, although it will always be available from the general login page.
Remember
First of all, thank you for using CARTO to unlock the potential of geospatial analysis with your students. We believe the new version of CARTO will carve out a new path for cloud native Location Intelligence for the years to come — Happy to have you onboard!
To update your teaching materials to reflect the new version of CARTO, we recommend you follow these simple steps. As an example, if your course content consisted of 5 datasets and 10 maps, updating content to the new CARTO platform shouldn’t take longer than a couple of hours.
Import the .CSV datasets you have exported
If you don’t have a data warehouse, you can use the CARTO Data Warehouse provided by us
Finally, using our new Builder tool, recreate the maps you’ll need for your course content.
We appreciate these updates require some time and effort on your side, but rest assured, we won’t be limiting access to your previous CARTO account any time soon.
If you're in a rush
From May 1st onwards, the signup process will be closed, but existing student and educator accounts will remain active. You can continue to use them for this semester, giving you time to update your materials for the next academic year.
CARTO offers comprehensive Support Packages to our enterprise customers. CARTO is committed to helping you make the most of your CARTO solution. These Packages are structured to help organizations at any stage of using location data to solve complex problems. We offer the following Packages with a combination of services to best suit your specific needs.
Users of the CARTO Platform are entitled to support according to their Support Package, described above. CARTO is not responsible for providing support to end users of CARTO-powered applications.
CARTO Support provides business hours coverage across 2 regions:
European Region ( 9am - 6pm Central European Time (CET) )
American Region ( 9am - 5pm Eastern Standard Time (ET) )
Business hours coverage is determined by your selected Support Package. For those customers on a Standard Package, you will be offered the option of selecting 1 of the regions as your indicated coverage times. For those customers on a Premium Package, “business hours” are defined by the indicated hours across both regions.
CARTO works in good faith to respond to all submitted issues in a timely fashion. Slower than usual responses can be expected due to regional holidays of our Team. Customers with an Elite Support Package are provided with 24/7 support for P1 priority issues (see more below on issue classification). Customers with an Elite Support Package will be given specific access to information and guidance regarding how and when to leverage 24/7 support.
Customer Success Managers at CARTO bring geospatial expertise and hands-on guidance in applying Location Intelligence to business needs, based on our experience working with hundreds of enterprise customers in diverse industries and fields. CSM’s also act as the “voice of the customer” communicating to and, as required, connecting customers with Product, Support, and other CARTO teams.
Support issues should be submitted via the indicated email address based on your selected Support Package.
All support issues received will be first triaged and assigned a prioritization level based on the severity of the reported issue. CARTO will work to first investigate and understand the issue at hand to ensure the appropriate severity level is assigned. CARTO classifies support issues as follows:
Customers should indicate the level of impact being experienced when submitting their support request. This will give CARTO’s Support Engineering team a sense of the potential impact and urgency of the issue. CARTO’s Support Engineering team will ultimately determine the issue severity based on initial investigation and correspondence with the issue submitter.
All response times are expressed in business hours, except for Elite P1 issues (*) that are expressed in regular hours.
Yes. As part of its SOC 2 Type II certification, CARTO undergoes annual auditing of its system and organization controls, performed by an independent, third-party certified auditor.
CARTO’s latest SOC 2 Type II report is available upon request for customers and prospects. Please note that prospects must have signed an NDA (Non-disclosure agreement) with CARTO before receiving the SOC 2 Type II report.
There are three ways for users to access their CARTO accounts:
Single Sign-On (SSO): In this case, your organization will define the password requirements and will leverage all security policies such as rotation, MFA, etc.
Sign in with Google: The password requirements and policies are defined in your Google account preferences, which may be managed by your organization.
Username/Password: CARTO uses Auth0 to securely process the data and enforces sufficient length and complexity standards.
CARTO is cloud-native by design, and we have no need to replicate your data — never. Maps, Workflows, and Applications built with CARTO will launch queries against live data in your own data warehouse (BigQuery, Snowflake, Redshift, Databricks, PostgreSQL, etc) and the result of these queries is not stored for further uses, with the exception of a temporal cache layer for performance and cost optimization, that is encrypted and distributed securely. This applies to all kinds of deployments.
To understand how CARTO processes your data we first need to describe the three categories of data that CARTO processes:
Connected Data: This is the data in your data warehouse (BigQuery, Snowflake, Redshift, Databricks, PostgreSQL, etc) that you'll be using in CARTO. As seen above, CARTO does not make any copies of your data. This data is encrypted in transit, and the credentials are never exposed in the frontend.
User-generated Content: These are the map details, workflows, credentials and configurations created by the users in a CARTO organization. User-generated Content is managed by CARTO. We carry out daily backups and encryption, except for self-hosted deployments. It is encrypted at rest and in transit.
Personal Data: This is the additional data needed by the platform to identify and provide service to the user such as settings, contact information, name, etc; User Data is managed by CARTO. We carry out daily backups and encryption, except for self-hosted deployments. It is encrypted at rest and in transit.
Connected Data: Stored in your connected cloud data warehouse, including the result of all analysis done in CARTO.
CARTO provides several controls to make sure viewers and editors don't gain unauthorized access to the underlying data of a map, workflow or application.
Editors can create connections to their data by providing credentials that are stored, encrypted, and never exposed in the browser in any case. These connections can then be shared with all editors in the organization (or with specific groups).
Maps, workflows and applications relying on a connection will stop working as soon as the credentials used are revoked.
Maps, workflows, and applications can be shared with all users within an organization (including viewers), or with specific groups, but this does not grant them access to the connection.
There are two different deployment options for the CARTO platform:
Self-Hosted: With this option, you host your own CARTO tenant. That means it can be deployed in your virtual private cloud (VPC) or behind your virtual private network (VPN).
(BETA) Snowflake Container Services: Fully deploy CARTO inside Snowflake by leveraging Snowflake Native Apps and Container Services.
While in CARTO Cloud updates and product releases are continously added to the platform, if you’re self-hosting your own CARTO tenant, it will need to be updated periodically to enjoy the latest version of the platform.
Here you will learn the basic concepts required to create a public web application using CARTO, compatible with any Javascript Development Framework. With CARTO you don't need to be a geospatial expert to develop a geospatial application, so if you're a web developer you shouldn't have any issues following this guide.
After completing this guide you will be familiar with the following concepts:
Scaffolding your application.
Publishing your app
CARTO is the leading Location Intelligence platform for the modern data stack. It enables organizations to use spatial data and analysis for more efficient delivery routes, better behavioral marketing, strategic store placements, and much more.
Data Scientists, Developers, and Analysts use CARTO to optimize business processes and predict future outcomes through the power of Spatial Data Science.
CARTO is the only cloud-first spatial platform built for accelerated, modern GIS. It runs natively on top of your cloud data warehouse platform (e.g. Google BigQuery, Snowflake, AWS Redshift, etc.), providing easy access to highly scalable spatial analysis and visualization capabilities in the cloud - be it for analytics, app development, data engineering, and more. CARTO is available in both cloud and self-hosted deployments.
Different type of users leverage our platform in different ways, such as:
We believe that CARTO does some things extremely well — And those things make us unique versus other geospatial platforms:
Depending on your usage of the CARTO platform, whether it’s for visualization, analysis, data access, or application development, you will be using different components of the platform.
The central location of all your experience with CARTO; connect to multiple cloud data warehouses, explore your geospatial data, geocode your tables, enrich your data with a wealth of vetted datasets to enhance your geospatial analysis, and access the different CARTO tools.
CARTO Builder offers powerful map making capabilities, interactive data visualizations, collaboration and publication options - everything running natively from your cloud data warehouse.
CARTO Workflows is a visual model builder that allows you to build complex spatial analyses and data preparation and transformation workflows without writing code. As with the rest of our platform, Workflows is fully cloud-native and runs in your own data warehouse.
For the Developer community, we have created a complete library of APIs, frameworks, connectors, and development tools to accelerate your spatial app development process.
The CARTO Analytics Toolbox is a suite of functions and procedures to easily enhance the geospatial capabilities available in the different cloud data warehouses. It contains more than 100 advanced spatial functions, grouped in different modules such as tiler, data, clustering, statistics, etc.
We catalog and distribute thousands of vetted public and premium spatial datasets, covering most global markets. These datasets are available across the different components of CARTO, so you can use them for data enrichment or as additional layers for your spatial apps and analyses.
Now that you're familiar with CARTO, here are some beginner-friendly next steps you can take to get started:
This guide introduces you to the core of CARTO Builder. From styling your layers to adding widgets and sharing an interactive map with other users; it's the ideal resource for both newcomers and those revisiting the foundational aspects of Builder.
The Maps section enables you to create and manage maps built with CARTO Builder in the CARTO Workspace.
From your Maps page, click Create your first map. This will open the CARTO's map-making tool, Builder.
Start by clicking on "Add Source from..." button. In CARTO Data Warehouse, navigate to demo_data > demo_tables and select “fires_worldwide” dataset.
Once your data source is selected, CARTO Builder will automatically generate the default layer.
It's a good practice to rename this layer for clarify. Let's call it "Fires".
Navigate to the Layer Style settings. Here, under the "Fill Color" option, you'll find "More Options". In this section, you can select "Frp" column to style the layer based on the fire radiative power. Remember to select a color palette that makes sense for your data.
For better visibility, specially at lower zoom levels, adjust the "Radius" to 1.5.
Widgets elevate the user experience by facilitating data exploration. Users can derive insights by interactive with interconnected filters that not only relate to each other but also adapt based on the map's viewport.
Enable Interactions on your layer. We will choose the "Click" option so that Interactions are displayed when users "click" over specific features within the map. Here you can also decide what specific information will be displayed in the pop-up as well as the labelling and formatting.
Adjust the legend to clarify the color indicator of your layer, such as "Fire Radiative Power (FRP)" for better understanding.
Under the "Layer Control", activate the "Layer Selector". Also, ensure the legend is set to display when the map is loaded by enabling the "Open the legend when loading the map" option.
With CARTO Builder, you have a wide range of choices when it comes to basemaps. For our current data, the CARTO Dark Matter provides an apt background, highlighting the fires.
Map descriptions are essential for providing context and enhancing user experience.
To edit the Map Description, click the "i" button at the top right corner. This will open an editable template. Our description field supports Markdown, allowing you to format text, insert links, images, and bullet lists for clearer, more engaging descriptions. For a preview, toggle to View mode by clicking the "eye" icon.
Before sharing, give your map a meaningful title. How about "Fires across the globe"?
Once named, the "Share" button becomes active. Dive into the "Sharing options" and pick the "Public map". This way, your map becomes accessible to anyone equipped with the link.
And voilà! Copy the map link, and you're ready to share.
These guides will help you get started with CARTO. They're easy to follow with detailed steps, and will help you kickstart your project with your own connections, maps, workflows, and applications.
In this first example we will create drive-time isolines for selected retail locations and we will then enrich them with population data leveraging the power of the H3 spatial index. This tutorial includes some examples of simple data manipulation, including filtering, ordering and limiting datasets, plus some more advanced concepts such as polyfiling areas with H3 cells and joining data using a spatial index in common.
As input data we will leverage a point-based dataset representing retail location that is available in the demo data accessible from the CARTO Data Warehouse connection (i.e. retail_stores), and a table with data from CARTO's Spatial Feature dataset in the USA aggregated at H3 Resolution 8 (i.e. derived_spatialfeatures_usa_h3res8_v1_yearly_v2).
Let's get to it!
In your CARTO Workspace under the Workflows tab, create a new workflow.
Select the data warehouse where you have the table with the point data accessible. We'll be using the CARTO Data Warehouse, which should be available to all users.
Navigate the data sources panel to locate your table, and drag it onto the canvas. In this example we will be using the retail_stores
table available in demo data. You should be able to preview the data both in tabular and map format.
In this example, we want to select the 100 stores with the highest revenue, our top performing locations.
First, we want to eliminate irrelevant store types. Drag the Select Distinct component from the Data Preparation toolbox onto the canvas. Connect the stores source to the input side of this component (the left side) and change the column type to storetype.
Click run.
Once run, click on the Select Distinct component and switch to the data preview at the bottom of the window. You will see a list of all distinct store type values. In this example, let’s say we’re only interested in supermarkets.
To select supermarkets, add a Simple Filter component from the Data Preparation toolbox.
Connect the retail stores to the filter, and specify the column as storetype, the operator as equal to, and the value as Supermarket (it's case sensitive).
Run!
This leaves us with 10,202 stores. The next step is to select the top 100 stores in terms of revenue.
Add an Order By component from the Data Preparation toolbox and connect it to the top output from Simple Filter. Note that the top output is all features which match the filter, and the bottom is all of those which don't.
Change the column to revenue and the order to descending.
Next add a Limit component - again from Data Preparation - and change the limit to 100, connecting this to the output of Order By.
Click run, to select only the top 100 stores in terms of generated revenue.
Next, add a Create Isolines component from the Spatial Constructors toolbox. Join the output of Limit to this.
Change the mode to walk, the range type to time and range limit to 600 (10 minutes).
Click run to create 10-minute drive-time isolines. Note this is quite an intensive process compared to many other functions in Workflows (it's calling to an external location data services provider), and so may take a little longer to run.
We now add a second input table to the canvas, we will drag and drop the table derived_spatialfeatures_usa_h3res8_v1_yearly_v2
from demo_tables
. This table include different spatial features (e.g. population, POIs, climatology, urbanity level, etc.) aggregated at H3 grid with resolution 8.
In order to be able to join the population data with the areas around each retail store, we will use the component H3 Polyfill in order to compute the H3 grid cells in resolution 8 that cover each of the isolines around the stores. We configure the node by selecting the Geo column "geom", configuring the Resolution value to 8 and enabling the option to Keep input table columns.
Next step is to join both tables based on their H3 indices. For that, we will use the Join component. We select the columns named h3 present in both tables to perform the join operation.
Check in the results tab that now you have joined data coming from the retail_stores table with data from CARTO's spatial features dataset.
As we now have multiple H3 grid cells for each retail store, what we want to do is to aggregate the population associated with the area around each store (the H3 polyfilled isoline). In order to do that we are going to use the Group By component, and we are going to aggregate the population_joined
column with a SUM as the aggregation operation and we are going to group by the table by the store_id
column.
Now, check that in the results what we have again is one row per retail store (i.e. 100 rows) and in each of them we have the store_id and the result of the sum of the population_joined values for the different H3 cells that were associated with the isoline around each store.
We are going to re-join with a Join component the data about the retail_stores (including the point geometry) with the aggregated population we have now. We take the output of the previous Limit component and we add it to a new Join component together with the data we generated in the previous step. We will use the column store_id
to join both tables.
In order to add an annotation to your canvas you only need to click on the corresponding icon on the top toolbar and select the location of the canvas where you want to add it.
Finally we use the Save as table component to save the results as a new table in our data warehouse. We can then use the "Create map" option to build an interactive map to explore this data further.
While using demo data during your onboarding process is great for learning and exploring the platform, nothing feels more real than using your own data in CARTO to create stunning maps, powerful analyses, and interactive applications.
But first, let's dig in a little bit to understand what happens when you connect your data to CARTO.
The CARTO platform is cloud-native by design. This means that we will always query the live data in your data warehouse, and your data warehouse will return the results, removing the need for ETLs and other costly and inefficient systems. We never make a copy or store the data on our servers, which means:
If you change the data in your data warehouse, your map will also reflect the changes (except cached results)
If you add to or modify the data in your data warehouse, it will also be immediately available in CARTO for you to create maps, workflows, and more.
Because of this, CARTO allows for unparalleled performance and scalability.
Now that we've reviewed the meaning and benefits of going cloud-native, let's create your first live connection to your data.
You can connect CARTO to your data in:
Google BigQuery
Snowflake
Amazon Redshift
PostgreSQL
Databricks (beta)
Check out this video to learn how to create your first connection. The video shows a Google BigQuery connection, but the process is similar when connecting to other data warehouses. Each step is also explained in detail below the video:
If your geospatial data is not yet in the cloud, CARTO can help you import it. There are many solutions to move data to the cloud data warehouses, but not many support geospatial formats, so let's take advantage of the CARTO platform.
Check out this video to learn how to import your first file. Each step is explained below the video:
Your data is now in CARTO! This is a major step toward unlocking all the potential that the platform has to offer. Using this data, there are a few options for what to do next:
(also valid for the CARTO Data Warehouse)
In CARTO Builder, you can use the Analytics Toolbox functions in your custom SQL queries when to your map.
Yes, you can. CARTO Data Warehouse connection works under the hood as a connection to Google BigQuery in the same region in which you have provisioned your CARTO organization account. Follow the same guides and reference for the to use this functionality from your CARTO Data Warehouse connection.
Yes, there are. The projects in which we install the Analytics Toolbox functions vary depending on the . In this you can find the BigQuery Project name for the Analytics Toolbox depending on the cloud region to which you have created a connection between CARTO and BigQuery.
Students can create a free CARTO account via . When they sign up for the pack, they’ll also get access to a ton of other free development tools! See the process and eligibility requirements below.
Educators are also eligible for a free CARTO account. Request an Educator account by completing the following , attaching a document that accredits your educator status. We welcome educators from accredited institutions as well as bootcamps and similar training organizations.
Academic researchers and others in the education field, whether at a school, university, independent research center, or boot camp, can make use of CARTO Enterprise at a discount. to learn more.
Sign up for a free Github account, using your university issued email to do so:
with your GitHub account
If you have any questions regarding Github’s verification process, please reach out to their support team at . Please also keep an eye on your spam folder, as your university email policies might route the verification message there.
🎉 Congratulations! You can now claim your free CARTO Student account here:
To login to your CARTO Student account you’ll need to always use this specific URL:
First, access your previous CARTO account:
Open the dataset(s) you need and export them in CSV format. More info .
Use your new credentials to access your CARTO account:
Follow our detailed guide for importing
For this semester: don’t panic! Students can still sign up for the previous version of CARTO using the student pack until April 30th 2022. They just need to follow this link:
Enterprise account users will contact Support with .
Elite accounts will have dedicated email addresses for P1. For P2 and P3 they will contact support with .
Visit to request the latest report.
Yes. CARTO complies with GDPR, CCPA and other data privacy laws where applicable. You can read more about it in our
If you're looking for password rotation, expiration or history controls we recommend you integrate , so that you can set up and leverage your existing company policies.
No, CARTO does not make any copies of the data available through your .
If you are using the , then it will be stored in the .
User-generated Content: This data is stored in the for SaaS deployments. For Self-Hosted deployments this is stored in your Self-Hosted resources.
Personal Data: Personal user data is stored securely in a server in the United States, on the Google Cloud Platform. You can read more about it in our
Editors can also for added security.
Published maps for additional security.
CARTO Cloud: A fully managed deployment that CARTO hosts on our own cloud. When you use CARTO in our cloud, we manage configuration, updates, and versioning. This option is available in different regions that you can select when .
Find links to the documentation and technical requirements .
The CARTO team publishes versioned releases on the public Self-hosted repositories that can be used to upgrade your deployment. Find the latest releases for and
Find links to the documentation .
You can find a step-by-step in the CARTO for Developers documentation:
Adding a
Creating an with limited access to your data warehouse.
Visualizing a dataset with
During this guide, we're using the . The process explained here is also compatible with other Warehouses like BigQuery, Snowflake, Redshift, or Postgres. Instead of using connection=carto_dw
, you need to use connection=<your_connection>.
A Data Analyst might use CARTO to join tabular data to geospatial data using and visualize the results in interactive maps built with , adding styles and context before sharing them with the rest of the organization.
A Data Engineer may use CARTO to automate to process and transform their geospatial data or to enrich their data with spatial datasets from the and adding the results to their cloud data warehouse with our native connectivity.
A Data Scientist might use CARTO to enrich their data with spatial data with , our and , to create new spatial features for machine learning models, and to run geo-statistics and advanced analyses with their geospatial data; sharing the results with interactive dashboards built in .
A Developer may build scalable and performant faster and on top of their own cloud data warehouse by using the CARTO module in and the CARTO APIs.
A GIS Analyst might use CARTO to assemble a visual to analyze data with weather warnings and automate the creation of a .
if you haven't already: our 14-day free trial does not require a credit card and allows you for unlimited testing.
Read our first-steps guides to , , and .
Discover our where you will find easy-to-follow steps to build your first use cases using the CARTO platform.
Begin your journey with , our dedicated tool for crafting and sharing interactive web maps using your geospatial data.
Configure a to calculate and display the total number of recorded fires.
Set up a to compare the total number of fires that started at night versus those during the day.
Create a based on the "bright_ti4" column to showcase the range and frequency of observed bright temperatures. Ensure you adjust the "Custom min. value" to 290 to filter out outliers.
is a visual model builder that allows you to build complex spatial analyses and data preparation and transformation workflows without writing code. As with the rest of our platform, Workflows is fully cloud-native and runs in your own data warehouse leveraging its full scalability.
In order to learn more about the main sections of CARTO Workflows' interface and its available components, please check of our documentation.
A cool feature in CARTO Workflows is the possibility to add in any area of the canvas, supporting the (allowing for different levels of headers, text formats, images, etc.). This allows users to better explain the different steps performed in a workflow so other users can understand them.
There are multiple ways to share the results of your workflows, from to to your colleagues. Additionally, note that from any step of your workflow (including that with the final saved table), you can create a map in in order to build an interactive dashboard with the result of your workflow plus any of your other spatial data sources.
Check our to keep learning how to get the most of this tool for your data transformation and analysis pipelines. The examples showcase a wide range of scenarios and applications: from simple building blocks for your geospatial analysis to more complex, industry-specific workflows tailored to facilitate running specific geospatial use-cases.
The main way to use your own data in CARTO is to , but if you still don't have a data warehouse (or if you don't have your geospatial files there) you can also .
If you still don't use any of these data warehouses (or you aren't ready to connect just yet), you can skip this part and go directly to .
If your data warehouse requires you to whitelist incoming connections, here is a link to .
You will also have the option to share your connection. Connections are private by default, but you can consider if you want to collaborate with other users.
Connections can be edited at any time, so don't worry about other advanced fields for now. Later in your CARTO journey, you will learn about the (our set of native geospatial functions for your data warehouse) and other exciting features.
❌ If unsuccessful: You will stay on the connections creation page and the error will give you more details about what's wrong. If you need assistance, our will be happy to help. Some things you should check:
🎉 Congratulations! You have now connected your data to CARTO. A quick way to test and explore this data is to open the and list tables coming from your data warehouse. If you click on a table you will immediately see metadata and a map preview. From here, you can start your next geospatial project!
Before you start with your data import process, please make sure you've checked the . A few additional best practices:
Check that your geometry data does not contain . These will be skipped in most cases, up to a certain threshold (see ), but could also cause the import process to fail.
If you're new to CARTO and you don't have any connections of your own, a safe way to get started is to import data into CARTO Data Warehouse > organization data > shared
. Here you can .
Customize the schema manually: You will see a preview, and you can customize the data type for each column. Read more about .
If there are rows with errors (e.g. invalid geometries, invalid values for a column, etc.), the process will continue without those rows until a certain threshold. Learn more about .
Finally, if there are too many errors or there's a major problem, an error block ❌ will appear with further details on why this import failed. If you need assistance, please contact our .
Create a stunning map using , our map-making tool.
Use to visually build a geospatial analysis block by block, with your data as a starting point or an input, with no coding skills required.
Use this data in a .
Support Access
email or videoconference
Documentation
✓
✓
✓
Support Coverage
Business Hours (1 region)
Business Hours (2 regions)
24 / 7
Target Response Times
Standard
Premium
Elite
Customer Success Manager (CSM)
✓
✓
Onboarding
Online
CSM-led
CSM-led
Success Plans
✓
✓
Quarterly Business Reviews
✓
✓
Access to CARTO-organized communities of practice
✓
✓
Product Updates
Online
CSM-led review
CSM-led review
Feature Request elevation
✓
Technical Advising Services
max. 40h
max. 80h
Classification
Description
P1
Critical issue; full service is unusable
P2
Issue with significant operational impact
P3
Issue with limited operational impact and general questions
Severity
Standard
Premium
Elite
P1
4
2
1*
P2
6
4
2
P3
16
8
6
Direct connection to your cloud data warehouse — no migration or ETLs needed.
Use native SQL across the platform and leverage your data warehouse geospatial capabilities.
Our Analytics Toolbox functions are installed and run natively in your data warehouse, expanding its geospatial capabilities.
Streamlined security and governance by inheriting data and user access controls.
Use much larger volumes of data, such as millions and billions of records, by leveraging your cloud data warehouse computational power.
Full support for spatial indexing techniques such as H3, optimizing transformations, enrichment, and analysis for superior performance with large datasets.
Create performant visualizations regardless of the scale of the data by using our dynamic and static tiling strategies.
Use Builder to create and share dashboards in minutes, with a complete set of tools and widgets.
Create spatial workflows easily in Workflows, our no-code visual model builder.
Easy to ramp up for people with limited exposure to geospatial, unlike traditional GIS tools.
Faster development of scalable geospatial applications by leveraging CARTO and Deck.gl, allowing you to focus on driving value with your application.
The CARTO Workspace is the user interface for our next generation cloud-native Location Intelligence platform. It allows you to create stunning maps and perform spatial analytics at scale, with everything running directly on top of your cloud data warehouse(s). Learn how to make the most out of our Builder tool, Analytics Toolbox, Data Observatory, and other new features.
Check out the following pages for setting up your organization, getting acclimated to the workspace, and creating your first map:
In this guide, we'll walk through the process of creating your first CARTO organization. This will be the first step to start creating stunning maps and perform spatial analytics at scale, with everything running directly on top of your cloud data warehouse.
Account sign up
Go to the CARTO Sign up page.
Enter your email address and password. You can also sign up with your existing Google account by clicking Continue with Google.
Follow the steps to verify your email and continue with your new organization setup.
Organization setup
If your email domain is already associated with an existing CARTO organization, you will be able to join (or request to join) any of the existing organizations associated with that domain. Alternatively, you will be able to create a brand new organization.
For new organizations, you will need to choose an organization name (e.g. CARTO) and a deployment region. As a rule of thumb, you should choose the region closest to your data warehouse. For more information, check our article on deployment regions.
After finishing the form, click "Let's get started with CARTO!" to complete the sign up process and get access to the CARTO Workspace. By signing up you accept the Terms and conditions of the Services and the privacy notice.
You are ready to start using CARTO!
For new CARTO cloud deployments, users can choose between four different regions. These correspond to four separate CARTO tenants, each located in a different geographical location:
United States East
Europe West (located in the EU)
Asia Northeast (located in Japan)
Australia Southeast
These tenants are fully managed CARTO deployments that we host on our own cloud (on the Google Cloud Platform). We manage configuration, updates, and versioning. Changes to CARTO are pushed simultaneously to these four tenants, so they are functionally equivalent.
All your organization’s data such as maps, workflows, applications, etc; will be stored in the selected region. In addition, the CARTO Data Warehouse of your organization will be located in that region as well.
There are a two main considerations when choosing a region:
Proximity to your data
Compliance and data regulations
Ideally, you should choose the region that is closest to your data to reduce latency and improve performance. For example, if you’re planning to connect CARTO to your Google BigQuery or Snowflake data in any US region, we recommend you choose the CARTO US region for optimal performance.
Some organizations might also have to choose a region that complies with specific data protection and privacy regulations, such as GDPR in the EU.
For additional help, you can use Google Cloud's Region Picker to help you select a cloud region.
The region of your organization is visible from the admin panel in the Organization Settings.
If you're not an admin, you can still check your organization's region by looking at the URL:
clausa stands for United States East
pinea for Europe West
thunbergii for Asia Northeast
radiata for Australia Southeast
Learn everything you need to know about your Workspace and how to make the most out of it.
When you log in to your CARTO user account, you will be presented with your Workspace. The Workspace allows you to access all components of the CARTO platform via a single interface. It will allow you to manage connections to your data warehouse(s), explore your data, subscribe to Data Observatory datasets, develop spatial applications, and run visualizations and spatial analysis through our tools Builder and Workflows.
The first time that you access the Workspace, you will see a Welcome banner with links providing quick access to different actions to get you started with CARTO, like creating your first connection or your first map and workflow, or starting with your spatial analysis in an easy guided way from our editable pre-built demo maps and demo workflows.
From the “Connect your data warehouse” banner, you can easily connect your data warehouse(s) to start using CARTO by clicking on Create new connection button. Check the quick guides to connect data and creating your first map to get started.
After creating your connection, you can also upload local files right from the homepage by clicking on Import your data button. Check this guide to start importing your data into your data warehouse.
From the “What's new” section, you will find announcements of new features, interesting articles, and the latest news related to CARTO from our blog. Stay tuned and don’t miss out on the latest news!
In this section you have a checklist with five quick steps to guide you to the different content pieces to help you get started with CARTO. Once you have completed all the steps, it will be marked as completed and you can close the panel by clicking on Close. If you want to skip the steps, just click on I´m ready, skip onboarding to close the panel.
The help sidebar is always available, including when you're creating Maps and Workflows. It contains:
An AI-powered search bar, similar to the one you can use in this documentation. Ask anything about CARTO and get quick answers, pointing in the right direction and including a link to this documentation.
Relevant links to our What's new section, the CARTO Academy, and the CARTO Support team.
View your latest content. This module displays the latest maps that you have been working on, so that you can quickly access and continue working on them.
If you are the owner of the map, you will have access to the quick actions menu to manage your map by clicking on the three dot icon of the map card. There are 4 options available: Edit map properties, Share, Duplicating maps and Delete.
View your latest accessed workflows. This module displays the latest workflows that you have been working on, so that you can quickly access and continue working on them.
View your latest datasets for easy access. This module displays the latest datasets that you have been working on, so that you can quickly access and continue working on them.
In the left panel, you can find the Navigation Menu with all the available options to access the CARTO components: Home, Maps, Data Explorer, Data Observatory, Connections, Settings, and Developers. In the bottom part of the menu, you have additional options to join the “CARTO Users” Slack channel, send us direct product feedback, or access the Documentation portal.
CARTO Builder allows you to add sources by connecting directly with your data warehouses, ensuring security and data governance. Once a source is added, the related layer associated with the source is also rendered on the map. From this point, you can start styling your layer, adding widgets, and creating your interactive application.
Builder currently support the following data sources types:
Simple features: Unaggregated features using standard geometry (point, line or polygon) and attributes, ready to use in Builder.
Spatial Indexes: Aggregated data sources for improved performance or specific use cases, including Quadbin and H3 spatial indexes.
Pre-generated tilesets: Tilesets pre-generated using CARTO Analytics Toolbox procedures or Workflows and stored directly in your data warehouse, ideal for handling very large, static datasets.
Raster: A raster source is composed of grids of pixels, where each pixel contains a value representing specific information
In Builder, you can add data sources either as table sources by connecting to a materialized table in your data warehouse or through custom SQL queries. These queries execute directly in your data warehouse, fetching the necessary properties for your map.
Table sources: Connect directly to your data warehouse table through the data explorer dialog. Once connected, the data source is added including its related layer.
SQL query sources: Perform a custom SQL qeury that will act as your input source. Once you execute it, if valid, a new data source and its linked layer will be added to the Builder.
Best practices for SQL Query sources
SQL Editor is not designed for conducting complex analysis or detailed step-by-step geospatial analytics directly, as Builder executes a separate query for each map tiles. For analysis requiring high computational power, we recommend two approaches:
Materialization: Consider materializing the output result of your analysis. This involves saving the query result as a table in your data warehouse and use that output table as the data source in Builder.
Workflows: Use CARTO Workflows for conducting step-by-step analysis. This allows you to process the data in stages and visualize the output results in Builder effectively.
To add sources in Builder, click on "Add source from" and choose from the following options:
Data Explorer: Browse and add tables as sources from your existing connections or from CARTO Data Observatory.
Custom Query (SQL): Write your own SQL query using the connection of your choice.
Import file: Start the process of importing a file to a CARTO connection.
Partitioned BigQuery tables require a WHERE clause in the query filtering by the column used for the partition. If you need to load a BigQuery partitioned table in Builder, add it as a SQL Query source like:
When adding a source from Data Explorer, you have the option to add a source from your existing connection or by adding data from the Data Observatory.
To add a source, navigate to the desired location, select your table and click 'Add Source'. The source and its associated layer will be added to the map.
Subscriptions are only available from CARTO Data Warehouse, BigQuery and Snowflake connections, while samples are only available from CARTO Data Warehouse and BigQuery connections.
Use the SQL Editor panel in Biulder to add a source by selecting a specific connection. Create your own SQL queries to perform simple analysis, create WHERE statements to pre-filter your data, or use SQL Parameters. You can also leverage CARTO Analytics Toolbox directly from this interface.
CARTO allows creating tables in your connectiosn by importing files from your computer or via URL. Once a file is imported, the resulting table can be previewed in Data Explorer and used in Builder and external applications.
Find more information about compatible data warehouses, supported formats, column names, and delimiters in our Importing Data documentation.
The CARTO platform natively supports spatial indexes, enabling you to leverage their capabilities when working with large-scale data directly in your data warehouse. Spatial indexes are excellent for large-scale analytics and visualization. Using Builder, you can create stunning and powerful visualizations by connecting directly to these types of sources. The supported spatial index formats are quadbins and H3.
Based on Discrete Global Grid (DGG) systems, spatial indexes reference each cell of the grid. Think of a spatial index as an id that always makes reference to the same portion of the surface on Earth.
This portion of the Earth is called a cell.
The shape of the cell depends on the type of index. For example, H3 uses hexagons; while Quadbin uses square.
The size of the cell depends on the resolution. The higher the resolution, the smaller the size of the cell.
DGG systems are hierarchical, which means that every cell contains a constant number of smaller cells at a higher resolution:
One of the advantages of working with spatial indexes is that operating with them in data warehouses is way more efficient and cost-effective than computing geometries. They are also smaller in size and help saving storage and reducing the volume of transferred data.
When working with spatial indexes, Builder will dynamically aggregate your data into cells at a meaningful resolution depending on the current map zoom level. See the animation below for an example:
Your spatial index source must contain a column storing the spatial index identifier. Below is an example table containing h3 indexes, with some additional columns that contain aggregated socio-demographic data for each hexagon:
8a0c0036a49ffff
103.0
1344.56
8a0c002e4c0ffff
1093.0
2087.04
8a0c002e4caffff
209.0
3098.39
The h3
column contains the indexes for H3 cells at level 10. That’s what we call the native resolution of the data.
When you load a source in Builder, data is aggregated dynamically as you zoom in an out. This aggregation will be generated on the fly, using SQL queries that are pushed from CARTO into the data warehouse.
When visualizing a spatial index source in Builder, you can control the aggregation size, to define how granular you'd like the aggregation to be when navigating through the map as part of the layer styling configuration. Learn more in this section.
To understand more about performance and processing cost optimizations that should be applied to this specific source type, check this section.
The CARTO Workspace includes functionalities for creating and publishing maps in a simple manner, using the CARTO map tool: Builder.
Builder is designed to allow technical and non-technical audiences to visualize, explore, and filter large amount of location data in your browser.
This guide will teach you how to create a map in the CARTO Builder, and perform data analysis by adding data to a map, adding filters, and more.
In the Maps section of the Workspace, you will see the list of your current maps. If you haven’t created a map yet, you will see the following page:
To create a new map, click Create your first map. This will open the CARTO map tool: Builder.
CARTO Builder contains many features that guide you through the process of creating a map, changing the styling, and selecting how your data appears. Use the following task list as guide for some of the main CARTO Builder features:
Builder supports simple features stored as geometry or geography in cloud data warehouses. These simple features are defined as a standard which specifies digital storage of geographical data, usually point, line or polygon, storing both spatial and non-spatial features.
This table shows the current type of simple features (geometry
or geography
) supported on each data warehouse:
Geography
Geometry
BigQuery
✅
Not Supported
CARTO DW
✅
Not Supported
Redshift
Not Supported
✅
Snowflake
✅
Not Supported
PostgreSQL
Not Supported
✅
Databricks
✅ WKB Binaries
Not Supported
When working with simple features in Builder, ensure that your spatial column contains only a single type of geometry—either points, lines, or polygons. Mixing different geometry types within the same spatial column is not supported. To handle multiple geometry types, use separate sources for each type.
Builder ensures performance experience when rendering simple features on a map as data is loaded progressively via vector tiles. The data for these tiles is extracted by pushing down SQL queries to the data warehouse, and they are requested as you zoom in and out or pan the map.
Note these queries in Builder are cached. To understand more how caching works and different methods to keep your data fleshed, check this link in our documentation.
Find more information about performance consideration for this data source type in this section.
Pre-generated tilesets are sources with tiles that have been previously generated using either the CARTO Analytics Toolbox or Workflows. Both the creation and storage of these tilesets occur in the data warehouse. This type of data source is ideal for managing very large, static datasets. They are efficient, cost-effective, and provide high-performance visualization.
Builder supports the visualization of the four types of pre-generated tilesets:
Vector Tilesets: Pre-generated tilesets processed from point, line, or polygon tables for smooth, interactive maps.
Point Aggregation Tilesets: These tilesets aggregate point data with their properties into tilesets, perfect for visualizing dense point clusters.
Quadbin Aggregation Tilesets: Aggregated grid for scallable hierarchy management.
H3 Aggregation Tilesets: Aggregate quadbin indices into tilesets for scallable spatial hierarcy management.
To ensure optimizal visualization and performance, you must make use of the different parameters that allow you to have full-control of the tileset specification. To learn more about this type of source in terms of performance consideration, review this section.
When you add a source, Builder attempts to recognize if there is a spatial definition to render its associated layer. The spatial definition of your source depends on the column storing your spatial data and its specific type. Builder will automatically recognize the spatial data definition when possible. If not, you can manually define your data using the layer panel UI in the data section.
For Builder to automatically recognize the spatial data definition, regardless of whether the source is a table or an SQL query, your source must contain at least one valid spatial column with its related type as follows:
Column storing geometry type: This column should store either point, line, or polygon geometry in a valid format. The column can have any name.
Column storing spatial index ID: To be recognized by default, the spatial column should follow our naming convention:
H3: The column must be named h3
.
Quadbin: The column must be named quadbin
.
If Builder cannot automatically recognize a spatial data definition, you can still load it as a source in the map and use the UI to define your spatial column and type. Once your definition is set, you can click "Apply Selection" and Builder will use that definition to render your layer.
If your source contains multiple spatial columns, you can use the UI to decide which specific spatial column and type you want to use to render the layers associated with your source. For example, you might have a column containing the point geometry and additional columns representing the isochrones from that location.
Note: The spatial data definition is set at the source level; therefore, you must ensure consistency in the definition for all layers linked to that source.
Special considerations
The spatial data definition is set at the source level; therefore, you must ensure consistency in the definition for all layers linked to that source. If you change the definition but there are other layers coming from the same source, a modal will appear notifying you of the components in the map that will be affected if you proceed with the changes.
Note spatial source definition is not supported for pre-generated tilesets or raster sources.
Raster data is composed of grids of pixels, where each pixel contains a value representing specific information, such as temperature, elevation, or vegetation indices. These datasets can represent continuous surfaces or sparse data with defined no-data regions. The CARTO platform provides seamless tools for importing, analyzing and visualizing raster sources directly in your data warehouse.
To visualize raster sources, they must first be stored in your data warehouse using the CARTO raster loader. This tool ensures your raster files are properly uploaded and formatted for seamless integration with the CARTO platform. Learn more about preparing and uploading your raster data.
To optimize performance, raster sources should include overviews—lower-resolution versions of the raster data—enabling efficient visualization at different zoom levels. Additionally, if your raster source contains a color interpreter (e.g., palette, grayscale, or RGB), CARTO will automatically apply default styling based on the metadata, making it easier to quickly render the raster on your map.
Note the current limitations and specific requirements for raster sources:
Widgets are not yet supported for raster sources.
Raster sources cannot be queried directly through SQL functions, meaning they are not compatible with SQL Editor or SQL Parameters. This is similar to the behavior of pre-generated tilesets.
Raster sources are currently supported for Google BigQuery, Snowflake and Databricks environments.
Maintaining accurate analytics in your map visualizations depends greatly on the freshness of your data. This section will delve into how Builder ensures your data remains current and will detail the options you have for refreshing your data sources.
Builder makes it straightforward to manage data freshness right from the initial map load. Data caching is enabled by default, varying by data type and warehouse provider, but you have the liberty to set specific freshness intervals for your map's data sources.
Default freshness settings
SQL Query sources: By default, data is cached for one year across all connections. If your data remains unchanged, it will be automatically refreshed after a year.
Table sources: The duration of data caching varies depending on your data warehouse provider:
BigQuery and Snowflake: Requests are cached for a minimum of 5 minutes. CARTO continues to serve cached results if the table data hasn't been updated.
Redshift, Databricks, and PostgreSQL: Requests are cached for 30 minutes.
Pre-generated tileset sources: Data is cached for a year. It's automatically refreshed after this period if unchanged.
Raster sources: Data is cached for a year. It's automatically refreshed after this period if unchanged.
Customizing data freshness
Choose from predefined freshness periods for your data sources to ensure maps load with the most current data, providing reliable analytics.
Caching plays a crucial role in optimizing the performance and responsiveness of your map. Each component, such as a map layer, leverages its own cache to store data essential for its visualization. Here's how it functions:
Data Storage: When you view a layer within a specific map extent, the system caches the data retrieved by that particular query. This means that if you or another user views the same layer with the same map extent again, the system can quickly display the data from the cache without needing to re-fetch it from the data source.
Handling Changes: Any modifications to the viewport extent, adjustments to widget filters, or changes in SQL parameter inputs trigger a new query to the data warehouse for data that hasn't been cached yet. Once this query is executed, its results are stored in the cache for future use.
This caching mechanism ensures efficient data retrieval and visualization, significantly enhancing the user experience by reducing load times and improving the map's overall performance.
Refreshing your data sources couldn't be simpler. Whether you need to update all sources or just specific ones, Builder's "Refresh" options are designed for efficiency. Initiating a refresh reloads your data sources and their associated layers, clearing any previous cache and sending a new request to your data warehouse. This process guarantees you're always working with the latest data.
Important consideration
Manual refreshes will increase the amount of data processed in your data warehouse, which might have a significant cost associated to it.
The cached objects associated to the data source will be invalidated, and the SQL queries that were executed to generate them will be executed again.
Builder now enables you to modify the location or connection of your data sources easily using the 'Change data source’ option available in the source card. This functionality ensures that map configurations remain consistent as long as the updated data sources maintain the same field names and types. For any components where a property cannot be identified, the map component will revert to its default settings.
Table Data Sources:
Navigate to the source card panel and click on the three dots menu.
Select the ‘Change data source’ option and locate the new data source.
Once the new dataset is identified, click 'Change source'.
SQL Query Data Sources:
Access the three dots menu in the source card panel to update the connection.
Alternatively, in the SQL Editor panel, click on the connection information in the top-right corner to modify it.
Select your desired connection and click ‘Change Source’.
If the new data source contains the same fields with matching names and types, the map will retain its existing configuration, and no changes are expected. However, if some properties used in the map are missing in the updated data source, the following behavior should be expected:
Styling and Interactions:
If a property used in interactions (e.g., hover or click actions) is missing in the new data source, those interactions will be removed.
If a property used for layer styling (e.g., coloring by a column) is no longer available, the layer will revert to the default style.
Widgets:
Widgets linked to missing properties will remain visible, and their configurations will be preserved.
As an Editor, you can manually select a new column from the dropdown menu in the widget configuration panel or remove the widget entirely if no replacement column exists.
SQL Parameters:
The updated connection must have access to the tables referred to in your custom SQL query. If not, you’ll need to update the query to refer to the relevant table locations.
For SQL Parameters, ensure that the updated query is still valid so that parameter controls continue to work as expected.
In CARTO Builder, the Point layer visualization option allows you to render and style point features on your map. This layer type offers various advanced styling options to enhance the visual representation of your data such as creating heatmap or grid visualizations dynamically from your original source.
You have different visualization options when it comes to point data. Using this functionality, we allow users to dynamically aggregate the original source to:
Grid : Aggregated geometry into grid cells.
H3: Aggregated geometry into hexagonal bins.
Heatmap: Aggregated geometry by density.
Cluster: Aggregated geometry into circles.
If your data source contains identical geometries with varying attributes (e.g., weather stations or buildings), you can use the Aggregate by geometry functionality to aggregate your layer based on a distinct spatial column. Learn more in this section.
Customer markers allows you to set an icon or an image as a marker in your map, either a single marker or use multiple markers by property. Out-of-the-box options from Maki icons collection is readily available. Additionally, you can upload a custom .png or .svg file to be used as marker in the map.
You can modify the Radius/Size of your symbol using a simple slider when set to fixed, or by defining a radius range when configured by a property.
In this section you can define Color that will be used to fill your point symbol. You can set a simple color or use a color schema based on a given property to add depth and meaning to your lines. Additionally, you can adjust the fill opacity to your desired percentage for better visual effects.
The stroke of your line layer can be customized in various ways to suit your visualization needs:
Stroke Color: Set a simple color or use a color schema based on a given property to add depth and meaning to your lines. Additionally, you can adjust the stroke opacity to your desired percentage for better visual effects.
Stroke Weight: Define the stroke weight as either a fixed value or based on a given property. You can modify the stroke weight using a simple slider when set to fixed, or by defining a weight range when configured by a property.
You can add labels for your point layer visualization. It can be either single label or double label. You can style and set the label size as well as configuring the placement of the label.
_carto_point_density
attribute When working with point dynamic tiling sources in Builder, points are automatically aggregated for optimal visualization. The closer you zoom into the map, the more granular the view becomes, showing individual points.
In Builder, you can now leverage the automatically added _carto_point_density
property to style the radius, fill and stroke of your layer based on the number of points aggregated at each visible point.
In CARTO Builder, the Point layer visualization option allows you to render and style point features on your map. This layer type offers various advanced styling options to enhance the visual representation of your data such as creating heatmap or grid visualizations dynamically from your original source.
The stroke of your line layer can be customized in various ways to suit your visualization needs:
Stroke Color: Set a simple color or use a color schema based on a given property to add depth and meaning to your lines. Additionally, you can adjust the stroke opacity to your desired percentage for better visual effects.
Stroke Weight: Define the stroke weight as either a fixed value or based on a given property. You can modify the stroke weight using a simple slider when set to fixed, or by defining a weight range when configured by a property.
You can enable height visualization to extrude the height of your grid layer. When this is enabled, make sure to change the map view to 3D so you can see the features on this view mode.
Set a fixed height or set the height value based on a given property. You use the slider to multiple the height value according to your need.
In CARTO Builder, the Point layer visualization option allows you to render and style point features on your map. This layer type offers various advanced styling options to enhance the visual representation of your data such as creating heatmap or grid visualizations dynamically from your original source.
The stroke of your line layer can be customized in various ways to suit your visualization needs:
Stroke Color: Set a simple color or use a color schema based on a given property to add depth and meaning to your lines. Additionally, you can adjust the stroke opacity to your desired percentage for better visual effects.
Stroke Weight: Define the stroke weight as either a fixed value or based on a given property. You can modify the stroke weight using a simple slider when set to fixed, or by defining a weight range when configured by a property.
Cluster point aggregation allows you to dynamically group and display your point data as clusters, even when working with large-scale datasets. This type of visualization is ideal for simplifying complex data, identifying concentration patterns, and gaining insights by visualizing data density in a more digestible format.
In this section, you can define the cluster radius range and adjust the symbol’s aggregation size. This allows you to control the level of detail in the clustering—lower values result in more detailed, granular clusters.
In the Stroke section, you can customize the stroke color and adjust its opacity. Additionally, you can set the stroke weight to match your visualization needs.
Labels for cluster layers allow you to display the number of aggregated points within each cluster. You can customize both the text color and the halo (outline) color to fit your visualization needs.
Grid point aggregation allows you to dynamically visualize your point data as an aggregated grid, leveraging CARTO's native support for spatial indexes. This type of visualization is ideal for simplifying large datasets, improving performance by reducing rendering complexity, and identifying patterns and trends within the data.
For this type of layer, there is an additional COUNT
aggregation operation available for numeric properties. In order to ensure a precise count, our recommendation is to use a unique id column in your data.
When using Grid layer visualization type in Builder, data is aggregated as you zoom in and out on the map. In the Cell section, you can define the aggregation size to determine the level of granularity you prefer for your data aggregation.
The stroke of your line layer can be customized in various ways to suit your visualization needs:
Stroke Color: Set a simple color or use a color schema based on a given property to add depth and meaning to your lines. Additionally, you can adjust the stroke opacity to your desired percentage for better visual effects.
Stroke Weight: Define the stroke weight as either a fixed value or based on a given property. You can modify the stroke weight using a simple slider when set to fixed, or by defining a weight range when configured by a property.
You can enable height visualization to extrude the height of your grid layer. When this is enabled, make sure to change the map view to 3D so you can see the features on this view mode.
Set a fixed height or set the height value based on a given property. You use the slider to multiple the height value according to your need.
Working with aggregated property values
When working with H3 point aggregation layer, all properties used for visualization and styling purposes must use a defined aggregation method. You can access this while using the column drop-down menu.
For this layer type there is an additional COUNT aggregation operation available for numeric properties. In order to ensure a precise count, our recommendation is to use a unique id column in your data.
H3 point aggregation allows you to dynamically visualize your point data as an aggregated hexagonal bins, leveraging CARTO's native support for H3 spatial indexes. This type of visualization is ideal for simplifying large datasets, improving performance by reducing rendering complexity, and identifying patterns and trends within the data.
Please note that the h3-pg PostgreSQL extension is required for dynamically aggregating points into H3 cells from PostgreSQL data sources.
When using Grid layer visualization type in Builder, data is aggregated as you zoom in and out on the map. In the Cell section, you can define the aggregation size to determine the level of granularity you prefer for your data aggregation.
The stroke of your line layer can be customized in various ways to suit your visualization needs:
Stroke Color: Set a simple color or use a color schema based on a given property to add depth and meaning to your lines. Additionally, you can adjust the stroke opacity to your desired percentage for better visual effects.
Stroke Weight: Define the stroke weight as either a fixed value or based on a given property. You can modify the stroke weight using a simple slider when set to fixed, or by defining a weight range when configured by a property.
You can enable height visualization to extrude the height of your grid layer. When this is enabled, make sure to change the map view to 3D so you can see the features on this view mode.
Set a fixed height or set the height value based on a given property. You use the slider to multiple the height value according to your need.
Working with aggregated property values
When working with H3 point aggregation layer, all properties used for visualization and styling purposes must use a defined aggregation method. You can access this while using the column drop-down menu.
For this type of layer, there is an additional COUNT
aggregation operation available for numeric properties. In order to ensure a precise count, our recommendation is to use a unique id column in your data.
Heatmap point aggregation allows you to dynamically display your point data as a heatmap visualization, even when working with large-scale data. This type of visualization is ideal for simplifying complex datasets, identify hotspot patterns and gain insights from your data.
The area of influence for heatmap layers defines the radius around each point that contributes to the heatmap. A smaller radius results in a smoother heatmap with lower detail, whereas a larger radius shows more distinct variations. This radius can either be uniform or vary according to a specific property.
Data sources with a spatial column storing H3 identifiers and a spatial type of H3 are rendered as an H3 layer type in CARTO Builder. H3 layers leverage the H3 cell ids to efficiently render features on the map in an aggregated format, enabling seamless visualization of large-scale spatial datasets.
When using Grid layer visualization type in Builder, data is aggregated as you zoom in and out on the map. In the Cell section, you can define the aggregation size to determine the level of granularity you prefer for your data aggregation.
The stroke of your line layer can be customized in various ways to suit your visualization needs:
Stroke Color: Set a simple color or use a color schema based on a given property to add depth and meaning to your lines. Additionally, you can adjust the stroke opacity to your desired percentage for better visual effects.
Stroke Weight: Define the stroke weight as either a fixed value or based on a given property. You can modify the stroke weight using a simple slider when set to fixed, or by defining a weight range when configured by a property.
You can enable height visualization to extrude the height of your grid layer. When this is enabled, make sure to change the map view to 3D so you can see the features on this view mode.
Set a fixed height or set the height value based on a given property. You use the slider to multiple the height value according to your need.
Working with aggregated property values
When working with H3 point aggregation layer, all properties used for visualization and styling purposes must use a defined aggregation method. You can access this while using the column drop-down menu.
For this type of layer, there is an additional COUNT
aggregation operation available for numeric properties. In order to ensure a precise count, our recommendation is to use a unique id column in your data.
When working with layers in Builder, you have the following options:
Zoom to: Zoom to the layer extent, taking into account any filtering applied in Widgets and/or Parameters when applicable.
Show only this/Show all layers: Easily set layers visibility on and off.
Layer style: Access the layer panel to set your layer styling configuration.
Duplicate layer: Duplicate a layer with the same styling properties.
Rename: Edit the name of your layer.
Delete: Remove the layer and its corresponding source.
The spatial definition of the source linked to a layer specifies the layer visualization type and additional visualization and styling options. The different layer visualization types supported in Buider are:
Control the zoom range where a layer should be visibile. This is useful for combining different type of sources, such as aggregated data for lower zoom levels and non-aggregated data for higher levels or visualizing different administrative levels.
If you are working with point, polygon or line layer visualization types containing identical geometries with varying attributes (e.g., weather stations or buildings), you can use the Aggregate by geometry functionality to aggregate your layer based on a distinct spatial column. This allows you to:
Aggregate geometries in your layer ensuring an optimal performance.
Aggregate styling and interaction attributes to retrieve relevant information link to your aggregated feature.
Maintain widgets functionality over the original source, enabling drill-down operations for deeper insights.
Layer styling is essential for making your maps informative and engaging. Below are generic aspects of visualization and styling options available in Builder. For more detailed styling capabilities for a specific layer type, we recommend to check each layer type as defined above.
When styling layers in Builder, you can choose a few different types of color palettes:
Diverging: Highlight values that are above and below an interesting mid-point in quantitative data. This is a great way to show data values that differ greatly from the norm. For example, you may use a diverging colour scheme to show population change.
Sequential: Ideal for data that follows an order, often numeric ranging from low to high. For example, you may use a sequential colour scheme to show counts within a H3 grid.
Qualitative: Represents different categories of data. For example, a qualitative scheme is a good choice for showing different types of Points of Interest.
Singlehue: Gradual transition of a single color from light to dark. For example to visualize the quality network coverage signal.
Custom: Pick a new color either by clicking on the color picker or inputting HEX/RGB values. Color steps can be added, removed and shuffled.
Connections to Redshift clusters only support aggregation of categorical properties by any value.
Admins can also create custom color palettes from the organization settings. These are reusable color schemes and they are available to the whole organization, removing the need to define a new custom palette every time a custom set of colours is used for styling.
You can also tap into the HexColor feature to style qualitative data using the hex color codes from either your table or SQL query source. To harness this capability:
Navigate to the Color based on selector and choose the text column you want to associate with the hex color code.
In the Palette section, select the 'HexColor' option.
Finally, choose the column containing the hex color code values.
Depending on the property selected to define your color schema, you have different color scale functionalities to define the color classification method.
For numeric columns, you can choose the following data classification methods:
Quantile: A quantile color scale is determined by rank. A quantile classification is well suited to linearly distributed data. Each quantile class contains an equal number of features. There are no empty classes or classes with too few or too many values. This can be misleading sometimes, since similar features can be placed in adjacent classes or widely different values can be in the same class, due to equal number grouping.
Quantize: A quantized color scale is determined by grouping values in discrete increments. It allows to transform an initially continuous range into a discrete set of classes. Quantize scales will slice the domain’s extent into intervals of roughly equal lengths.
Custom: A custom color scale is determined by arbitrary breaks in the classification. A custom scale is well suited to tweak color ramps, adjusting the values to fine tune the visualizations.
For text columns, you can use the Ordinal classification method to set a specific category to each color value:
Builders allows you to assign heights to build 3D visualization for both polygons and spatial index sources. You can activate this option in the Height section, using the slider to define a fix value or using a property to define the height.
When using the Height functionality, remember to activate the 3D view located in the toolbar above the map. Using this, you can achieve stunning visualizations as per below map.
Layer blending is a technique used to determine how overlapping features in different layers interact in terms of their visual representation. When two layers are blended, you can select the following blending options:
Additive: This mode adds the color values of overlapping features. When two colors are added together, the resulting color is often lighter. This blending mode is commonly used to visualize densities or intensities.
Subtractive: This blending mode subtracts the color values of the upper layer from the layer beneath it. The result is typically a darker color. In some contexts, this mode can help emphasize differences between layers.
Within the Advanced visualization options , you can access Zoom Visibility to define the range at which your layer should be visualized. This ensures that your lines are displayed at appropriate zoom levels, improving the clarity and relevance of your map.
When configuring your point layer symbol, you either use a simple point shape to render your point layer or you can use a custom marker .
When configuring either the color based on a property, you can access Advanced fill options to set the color scale. This allows for a more granular and informative visualization.
When configuring either the stroke color or stroke weight based on a property, you can access Advanced stroke options to set the color scale or weight scale. This allows for a more granular and informative visualization.
Within the Advanced visualization options , you can access Zoom Visibility to define the range at which your layer should be visualized. This ensures that your lines are displayed at appropriate zoom levels, improving the clarity and relevance of your map.
If your data source contains identical geometries with varying attributes (e.g., weather stations or buildings), you can use the Aggregate by geometry functionality to aggregate your layer based on a distinct spatial column. .
In this section you can define Color that will be used to fill your point symbol. You can set a simple color or use a based on a given property to add depth and meaning to your lines. Additionally, you can adjust the fill opacity to your desired percentage for better visual effects.
When configuring either the color based on a property, you can access Advanced fill options to set the . This allows for a more granular and informative visualization.
When configuring either the stroke color or stroke weight based on a property, you can access Advanced stroke options to set the color scale or weight scale. This allows for a more granular and informative visualization.
When configuring the height based on a property, you can access Advanced height options to set the height scale. Additionally, you can enable the Show wireframe option to visualize the stroke of the 3D objects.
Within the Advanced visualization options , you can access Zoom Visibility to define the range at which your layer should be visualized. This ensures that your lines are displayed at appropriate zoom levels, improving the clarity and relevance of your map.
If your data source contains identical geometries with varying attributes (e.g., weather stations, admin regions or buildings), you can use the Aggregate by geometry functionality to aggregate your layer based on a distinct spatial column. .
When configuring either the stroke color or stroke weight based on a property, you can access Advanced stroke options to set the color scale or weight scale. This allows for a more granular and informative visualization.
In the Visualization section you can specify the Opacity setting at layer source. Additionally, within the Advanced visualization options , you can access Zoom Visibility to define the range at which your layer should be visualized.
Define the Color that will be used to fill your cluster. You can set a simple color or use a based on a given property to add depth and meaning to your lines. Additionally, you can adjust the fill opacity to your desired percentage for better visual effects.
When configuring either the color based on a property, you can access Advanced fill options to set the . This allows for a more granular and informative visualization.
In the Visualization section, you can easily identify the type of layer your visualizing. Additionally, within the Advanced visualization options , you can access Zoom Visibility to define the range at which your layer should be visualized. This ensures that your lines are displayed at appropriate zoom levels, improving the clarity and relevance of your map.
In this section you can define Color that will be used to fill your cell. You can set a simple color or use a based on a given property to add depth and meaning to your lines. Additionally, you can adjust the fill opacity to your desired percentage for better visual effects.
When configuring either the color based on a property, you can access Advanced fill options to set the . This allows for a more granular and informative visualization.
When configuring either the stroke color or stroke weight based on a property, you can access Advanced stroke options to set the color scale or weight scale. This allows for a more granular and informative visualization.
When configuring the height based on a property, you can access Advanced height options to set the height scale. Additionally, you can enable the Show wireframe option to visualize the stroke of the 3D objects.
In the Visualization section, you can easily identify the type of layer your visualizing. Additionally, within the Advanced visualization options , you can access Zoom Visibility to define the range at which your layer should be visualized. This ensures that your lines are displayed at appropriate zoom levels, improving the clarity and relevance of your map.
In this section you can define the Color that will be used to fill your cell. You can set a simple color or use a based on a given property to add depth and meaning to your lines. Additionally, you can adjust the fill opacity to your desired percentage for better visual effects.
When configuring either the color based on a property, you can access Advanced fill options to set the . This allows for a more granular and informative visualization.
When configuring either the stroke color or stroke weight based on a property, you can access Advanced stroke options to set the color scale or weight scale. This allows for a more granular and informative visualization.
When configuring the height based on a property, you can access Advanced height options to set the height scale. Additionally, you can enable the Show wireframe option to visualize the stroke of the 3D objects.
In the Visualization section you can specify the Opacity setting at layer source. Additionally, within the Advanced visualization options , you can access Zoom Visibility to define the range at which your layer should be visualized.
You can style your heatmap choosing the desired palette in the Color section. For more information about color palettes supported in Builder check this .
In the Visualization section, you can easily identify the type of layer your visualizing. Additionally, within the Advanced visualization options , you can access Zoom Visibility to define the range at which your layer should be visualized. This ensures that your lines are displayed at appropriate zoom levels, improving the clarity and relevance of your map.
In this section you can define Color that will be used to fill your point symbol. You can set a simple color or use a based on a given property to add depth and meaning to your lines. Additionally, you can adjust the fill opacity to your desired percentage for better visual effects.
When configuring either the color based on a property, you can access Advanced fill options to set the . This allows for a more granular and informative visualization.
When configuring either the stroke color or stroke weight based on a property, you can access Advanced stroke options to set the color scale or weight scale. This allows for a more granular and informative visualization.
When configuring the height based on a property, you can access Advanced height options to set the height scale. Additionally, you can enable the Show wireframe option to visualize the stroke of the 3D objects.
Layers in Builder are connected to data sources and are used to render features on a map by directly connecting to your data warehouse. Once a data source is added to Builder, a layer is automatically added for that data source. If the spatial definition is valid, the features will be rendered on the map. Learn more about defining source spatial data in this .
: Displays as point geometries. Point data can be dynamically aggregated to the following types:
: Aggregated point geometry to grid.
: Aggregated point geometry to hexagonal cells.
: Aggregated point geometry by density.
: Aggregated point geometry by circles.
: Displays as polygon geometries.
: Displays as line geometries.
: Displays features as grid cells.
: Displays features as hexagon cells.
: Displays a grid of pixels.
When working with , you will need to select an aggregation operation for your columns.
For more information, see our .
For more information about how to leverage this functionality see this .
Logarithmic: A Logarithmic scale based on powers of 10
will be created automatically, based on the number of steps in the selected color palette. Logarithmic scales tend to work well with .
Data sources using spatial column storing quadbin identifiers and spatial type quadbin will be rendered as a Grid layer type. Grid layers uses quadbin to natively render features on the map in an aggregated manner.
When using Grid layer visualization type in Builder, data is aggregated as you zoom in and out on the map. In the Cell section, you can define the aggregation size to determine the level of granularity you prefer for your data aggregation.
In this section you can define Color that will be used to fill your point symbol. You can set a simple color or use a color schema based on a given property to add depth and meaning to your lines. Additionally, you can adjust the fill opacity to your desired percentage for better visual effects.
The stroke of your line layer can be customized in various ways to suit your visualization needs:
Stroke Color: Set a simple color or use a color schema based on a given property to add depth and meaning to your lines. Additionally, you can adjust the stroke opacity to your desired percentage for better visual effects.
Stroke Weight: Define the stroke weight as either a fixed value or based on a given property. You can modify the stroke weight using a simple slider when set to fixed, or by defining a weight range when configured by a property.
You can enable height visualization to extrude the height of your grid layer. When this is enabled, make sure to change the map view to 3D so you can see the features on this view mode.
Set a fixed height or set the height value based on a given property. You use the slider to multiple the height value according to your need.
Working with aggregated property values
When working with H3 point aggregation layer, all properties used for visualization and styling purposes must use a defined aggregation method. You can access this while using the column drop-down menu.
For this type of layer, there is an additional COUNT
aggregation operation available for numeric properties. In order to ensure a precise count, our recommendation is to use a unique id column in your data.
A Pie Widget offers a visual representation of categorical data as a circular chart, where each category is depicted as a slice of the pie. The size of each slice corresponds to its relative value within the whole dataset, making it a valuable tool for illustrating proportions and comparisons within the total data set.
When configuring the Pie Widget, you can either perform a simple COUNT
operation over your define category field or you can perform an aggregated operation using AVG
, MAX
, MIN
, or SUM
on your define numeric field for each of the specified categories. For example, you can calculate the AVG signal for each network type.
From Display options, you can also change the formatting of the values displayed. Additionally, you add some notes that support Markdown syntax to provide further context to users.
Within the "Behavior" section, you can define how your widget operates: choose between "Viewport" or "Global" modes. Additionally, you can make your widgets collapsible, allowing you to hide them when needed.
Moreover, you can conveniently enabled or disabled the filtering capability of your widget by using the cross-filtering toggle icon.
Learn more about widget behavior here.
You can use the Zoom to layer functionality in Builder to zoom your map to display all the features of a layer. This helps you quickly locate your layer, speeding up your exploration journey.
The Zoom to Layer feature can be accessed from the Layer Card and Layer Panel for Editors, and from the legend for both Editors and Viewers.
If your layer features have been filtered by Widgets or SQL Parameters, the zoom will always account for this filtering, allowing you to easily focus on the selected features.
Limitations
Please note that Zoom to layer is not supported for H3 spatial indexes in PostgreSQL and Redshift. Additionally, it is not supported for Databricks sources.
Zooming to layers linked to spatial index sources (H3 or Grid) and pre-generated tileset sources won't take into account the widget filtering status.
When a parameter control combination does not return any values, the Zoom to layer functionality will not work and will return an error message.
The eye icon in the Layer Card, Layer Panel, and Legend allows users to easily toggle layers on or off. For maps with many layers, the "Show only this/Show all layers" functionality is particularly useful. It enables you to switch off all other layers or make them all visible if only one layer is currently displayed.
Formula Widget allows you to derive Key Performance Indicators (KPIs) and metrics by performing operations directly from your source data.
When configuring the Formula Widget calculations, you have the flexibility to choose in the Data section from a provided aggregation list or to set a custom aggregation.
The provided aggregation list options include COUNT
, AVG
, MAX
, MIN
, and SUM
, offering a range of commonly used operations. When using these aggregation options, you simply need to specify the field from your data source that you want to aggregate.
For more complex and custom calculations, you can select the custom aggregation option to create your own SQL Expression. With this option, you can use a wide range of SQL functions, operators and syntax using single or multiple columns from your data source.
Custom aggregation option in Formula Widget is not available for pre-generated Tilesets.
From the Display options, you can set the format of the data displayed in the widget as well as adding notes which support Markdown syntax to provide further context your widget.
Within the Behavior section, you can define how your widget operates by choosing between "Viewport" or "Global" modes. Additionally, you can make your widgets collapsible, allowing you to hide them when needed. Learn more about widget behavior here.
A Category Widget summarize and compare categorical data using proportional bar lengths to represent values. The longer the horizontal bar, the greater the value it represents.
When configuring the Category Widget, you can either perform a simple COUNT
operation over your define category field or you can perform an aggregated operation using AVG
, MAX
, MIN
, or SUM
on your define numeric field for each of the specified categories. For example, you can calculate the SUM population for each land use category.
From Display options, you can also change the formatting of the values displayed. Additionally, you add some notes that support Markdown syntax to provide further context to users.
Within the "Behavior" section, you can define how your widget operates: choose between "Viewport" or "Global" modes. Additionally, you can make your widgets collapsible, allowing you to hide them when needed.
Moreover, you can conveniently enabled or disabled the filtering capability of your widget by using the cross-filtering toggle icon.
Learn more about widget behavior here.
In Builder, widgets empower users to dynamically explore data, leading to rich visualizations. They also serve to filter data based on the map viewport and interconnected widgets.
Below are the current type of Widgets available to customize your visualization and enable a richer interaction with your data:
Formula Widget: Shows aggregated numerical data as a single metric.
Category Widget: Segments data into distinct categories displaying aggregated metrics.
Pie Widget: Visualizes categorical data by displaying the proportion of each category relative to the whole data set.
Histogram Widget: Shows the frequency distribution across equal bins in the data range.
Range Widget: Shows a specific range of numerical data adjustable via slider or input values.
Time Series Widget: Shows the frequency distribution aggregated by a temporal period. It also allows to create animated maps.
Table Widget: Displays tabular data for easy viewing and interaction.
Please note that widgets for raster sources are not yet supported.
Add a widget to Builder by clicking "New Widget" and select your data source.
Then, select a widget type from the menu: Formula, Category, Histogram, Range, Time Series or Table.
Once you have selected the widget type of your preference, you are ready to configure your Widget.
In the Data section of the Widget configuration, choose an aggregation operation COUNT
, AVG
, MAX
, MIN
or SUM
and, if relevant, specify the column on which to perform the aggregation.
When working with pre-generated tilesets, please ensure your data have a unique identifier named geoid
for correct Widgets calculations.
Using the Formatting option, you can auto-format data, ensuring enhanced clarity. For instance, you can apply automatic rounding, comma-separations, or percentage displays.
You can use Notes to supplement your Widgets with descriptive annotations which support Markdown syntax, allowing to add text formatting, ordered lists, links, etc.
Widgets offer two distinct modes of operation: "Global" and "Viewport".
Global Mode
By default widgets are calculated in global mode where information is displayed for the full data source. This option does not take into account the viewport extent of the data.
Viewport Mode
You can configure widgets to work in viewport mode, meaning the data gets updated when the viewport extent changes.
For dynamic tiling sources, viewport widgets get the data by performing a SQL query to the data warehouse; whereas, if you are working with pre-generated tilesets, viewport widgets work with the data that has been downloaded for visualization which is available locally in the browser.
Please be aware that global mode is not supported for pre-generated tileset sources. In such scenarios, the functionality defaults to viewport mode. This means that all calculations are based on the extent of the map's current viewport.
In the Behavior section of Builder, you have the option to make Widgets collapsible, allowing them to be hidden when needed. Additionally, widgets can be set to automatically collapse when their associated layers are not visible. Moreover, certain widgets possess the ability to concurrently filter themselves and other widget linked to the same data source. This filtering functionality can be conveniently enabled or disabled for individual widgets using the cross-filtering toggle icon.
As Widget settings differ between widget types, please visit the individual widget's documentation page for more detailed information.
The Histogram Widget visually summarize the distribution of a continuous numeric variable by measuring the frequency at which certain values appear in the dataset.
The x-axis of the Histogram Widget has been split into buckets. For each bucket, a bar is drawn where the width of the bar represents the range of the bucket, and the height of the bar represents the number of features that fall into that range.
In the Data section of the widget configuration, select a continuous numeric field. It's advisable to select a numeric variable with a continuous distribution for optimal results.
In the Display options section, you can customize the number of buckets, which helps you control the granularity of your data representation. Additionally, you can also customize the limit values of your data that can be useful if you are looking to filter some features out your visualization.
In the display section, you can also use formatting to choose the unit of metrics displayed on the y-axis. Additionally, you can add notes that support Markdown syntax.
Within the "Behavior" section, you can define how your widget operates: choose between "Viewport" or "Global" modes. Additionally, you can make your widgets collapsible, allowing you to hide them when needed.
Moreover, you can conveniently enabled or disabled the filtering capability of your widget by using the cross-filtering toggle icon.
Learn more about widget behavior here.
In CARTO Builder, the Raster layer visualization option allows you to render and style raster data such as such as deforestation patterns, land classifications, or flood risk models, directly on your map.
You can also use Visibility by zoom level to define the range of zoom levels at which your layer will be displayed. This ensures your map remains relevant by showing layers only at the appropriate scales.
A raster layer can be styled in the following manners:
Color Range: Ideal for continuous data like elevation or temperature ranges.
Unique: Best for categorical data such as land use classification or vegetation types.
RGB: Suitable for visualizing satellite imagery sources to obtain true-color or false-color composite.
Color Range allows you to style a band by different ranges. The Color Range will be used by default if you have 'Grey' as the Color Interpreter in your raster metadata band. Learn more about Color Interpreter here.
Quantiles color scale is available only if the quantile
stat has been added to the specific band during the raster-loader process.
Unique style allows you to style discrete numeric bands using unique color values. The Unique style will be used by default if you have 'Palette' as the Color Interpreter by default. Learn more about Color Interpreter here.
Unique style is only available for rasters containing at least a discrete numeric band containing top_values
stat in the raster band metadata.
RGB styles allows you to assign bands to the red
, green
and blue
channel spectrum. The RGB style will be used by default when red, green and blue bands are color interpreter in the metadata. Learn more about Color Interpreter here.
Here you can select specific bands as well as adding your own custom expressions. To add a custom expression, simply type in the selector menu the expression you want to add. You must ensure the syntax is correct before adding an expression.
If your original raster includes a Color Interpreter, this will be respected during the uploading process and will be used by default to style your raster layer in CARTO Builder. The current Color Interpreter types supported, based on GDAL data model, are as follows:
Palette: For discrete numeric bands, the Unique style will be applied by default. This type supports a Color Table, which defines the relationship between numeric values and colors.
Gray: For bands with a gray interpreter, a grayscale palette is applied using the Color Range and Quantize color scale for smoother visualization.
Red, Green, and Blue (RGB): When bands are interpreted as red, green, and blue, the RGB style is used by default. Each band is assigned to the corresponding red, green, or blue channel to render a composite image.
CARTO Builder will not render any pixels assigned the No Data Value for each band. For example, if the no data value for a specific band is set to 0
, any pixels with this value will not appear in the visualization.
For RGB rasters, the no data value must match across all three bands (red, green, and blue) for a pixel to be ignored. For instance, if the no data values are set to 0
for these bands, the corresponding pixel will be excluded from rendering only when all three bands have a value of 0
.
Learn more about data preparation and no data value assignment in this section.
Dates parameters are replaced by date values in your queries. They are always used in pairs, defining the start and the end of a period of time.
When creating a dates parameter, there are a few settings to be defined:
Define the dates that will be available for selection in the control UI. The calendar will be limited to the dates defined in this setting.
Display name: The name that will appear in the control UI.
SQL name(s): The name to be used in the SQL query to be replaced by the starting and ending date of the selected period.
Once the parameter has been created, it should be added manually (using its SQL names) to one or more of your SQL Query data sources, like:
After adding the parameter to your SQL query data sources, the control UI will appear in the right-side panel, allowing to define the min and max pair of date values:
Text parameters are replaced by an array of strings in your query. They are a good choice if you need to filter by an existing category in your data or a text identifier.
When creating a text parameter, there are a few settings to be defined:
In this section we can define the list of values that will be available for selection in the control UI.
Add manually: Directly add values into the list.
Add from Source: Select a column from your source to automatically add up to the most frequent distinct values to the list. Please note there is a cap of 1,000 values when retrieving using this method.
The above methods can be used in combination.You can start by manually adding values, then use the 'Add from Source' option to extend your existing list with additional values, and adjust it as necessary.
Display name: The name that will appear in the control UI.
SQL name: The name that needs to be used in your SQL query. It always has to be enclosed between double curly brackets, like: {{type_of_place}}
.
Once the parameter is created, we should manually add it (using its SQL name) to one or more of your SQL Query data sources, like:
After adding the parameter to your SQL query data sources, the control UI will appear in the right-side panel, allowing search, custom text input and multi-selection of values:
Text parameters in your queries are replaced by an array of strings with all the values selected using the control UI.
Make sure to use a SQL syntax that works well with arrays, like the IN
operator
The Table Widget offers a clear, structured view of your dataset, enabling easy data exploration. You can search, highlight features by hovering, and zoom to specific features by clicking, all while seamlessly connecting the table to the map.
In the Data section, you can specify which properties to display in the Table view, allowing you to set labels and customizing the format as needed.
The Display section allows you to add notes that support Markdown syntax for better clarity and customization. Within the widget, you can choose the number of rows displayed per page and use pagination to view additional data. Sorting can be applied to the columns in either ascending or descending order.
You can also configure whether the widget is collapsible and if it should be automatically hidden when the linked layer is not visible on the map.
n the Behavior section, you can choose between two modes:
Viewport Mode: Shows only features visible in the current map view. Both displayed data and search are limited to features within the viewport.
Global Mode: Displays all features from the data source, allowing you to search and zoom to any feature, even if it’s outside the current map view.
If a mask is applied using the Selection tool, this will override both the Global and Viewport modes. In this case, both the displayed data and search results will be limited to the features within the mask's extent.
Learn more about widget behavior here.
Time Series Widget allows you to identify insights and patterns from your data over a period of time. To use it, ensure your data source includes a valid timestamp.
When configuring a Time Series Widget, you have the flexibility to choose from a provided aggregation list that includes COUNT
, AVG
, MAX
, MIN
, and SUM
to define the metric to be displayed in the widget.
Multiple series can be visualized in the widget to compare trends. You can create multiple series using one of the following options: "Split by" and "Add metrics"
Split by: To split a single series into multiple segments, simply specify a "Split by" category field. For instance, in a dataset tracking traffic accidents, you define your primary metric as the "COUNT" of records and use the "Split by" category column as "accident_severity". The widget will then present the "COUNT" of each unique accident severity category as separate series.
Add metrics: If you want to incorporate more data dimensions into your visualization, utilize the "Add metrics" option. For example, in a traffic accident dataset, you can define your initial metric as the "SUM" of cycles affected at accident spots. Subsequently, you can add another metric to calculate the "SUM" of motorcycles affected. The resultant widget will display two distinct series: one representing the sum of cycles and the other representing the sum of motorcycles for each specified time interval.
Note that the Time Series widget can support up to 10 different series. When using split by option, the most dominant categories will be automatically selected.
Temporal data is organized into discrete time intervals along the x-axis of the widget. By default, an interval size is automatically selected, but you have the flexibility to manually adjust it as needed.
You can empower users to animate moving geometry features by adding animation controls. You can click the clock icon to adjust speeds: 0.5x, 1x, 2x, or 3x.
Animation support on the Time Series widget is currently not available for aggregated sources such as point aggregated such as heatmap, cluster, etc. or aggregated spatial index sources such as h3 or quadbin.
In the display section, you can also use formatting to choose the unit of metrics displayed on the y-axis. Additionally, you can add notes that support Markdown syntax.
Within the "Behavior" section, you can define how your widget operates: choose between "Viewport" or "Global" modes. Additionally, you can make your widgets collapsible, allowing you to hide them when needed.
Moreover, you can conveniently enabled or disabled the filtering capability of your widget by using the cross-filtering toggle icon.
Learn more about widget behavior here.
When dealing with temporal data sources, we advocate the following best practices:
Large temporal data: When working with large temporal data, consider aggregating your data to a higher temporal granularity and using Time Series Widget in combination with Date SQL Parameter. These recommendations can help reduce the volume of data and enhance performance
Static geometries: If you are working with static geometry whose attributes change over time, we recommend you aggregate your source grouping by geometry to avoid displaying duplicated geometries on the map. You can also leverage the "Date" type of SQL Parameter to dynamically define the date range of your source.
Animation: Animation is primarily recommended for geometries that exhibit movement over time. If you are interested on animation for static geometry whose attributes vary over time please let us know.
Time Series Widget is not available for pre-generated Tilesets . Note also that only one Time Series Widget per map is permitted.
Legends and layer lists are essential for geospatial data visualization, providing context, enhancing map interpretation, and enabling quick actions such as toggling visibility and zooming.
In the Legend tab within Builder, you can control how layers and their legends appear in the map layer list:
Layer visibility: Toggle whether a layer is included in the map layer list for users to interact with.
Legend visibility: Independently toggle whether a layer’s legend is displayed in the map layer list.
Custom labels: Edit legend labels to provide clear and meaningful descriptions for each layer.
Pre-generated tileset layers styled with HexColor are not currently supported in the legend. If you require this functionality, please provide feedback through your CARTO point of contact.
The Range Widget enables precise data filtering, allowing you to define specific numerical ranges.
In the Data section, select a numeric field from your source dataset that you want to analyze.
In the Widget Display, the default range corresponds to the minimum and maximum values of the selected numeric field. This provides a starting point for your visualization. However, you have the flexibility to customize these limit values, allowing you to effectively filter specific features within your data.
Builder interactions allow you to configure various UI options to display the attributes associated with a layer. Interactions are disabled by default but can be enabled for each individual layer using the toggle button. Once enabled, you can choose from the following options:
Click: Users can extract insights by clicking on a feature rendered on the map.
Hover: Users can extract insights by hovering over a feature on the map.
For vector sources, features that are clicked or hovered over will be highlighted in a distinct style, provided the source includes a column named geoid
(serving as a unique identifier) or is of a spatial index type.
Highlighting features for interactions are not available for raster layers.
Interactions can be displayed in two formats:
Pop-up windows: Click and hoover-type interactions can be styled in various themes, such as light, dark ,etc.
Info panel: Click-type interactions can also appears in the right-side panel with a default style.
Interactions can be customized either manually or through custom HTML code, with support for properties in vector layers and both properties and custom expressions in raster layers:
Adding Properties Manually: For vector layers, users can set labels and customize formats for their properties. For raster layers, users can configure bands (properties) and also define custom expressions to enhance data visualization and analysis.
Using HTML Custom Code: This method offers flexibility for both vector and raster layers. While vector layers allow users to design tailored interaction layouts using properties, raster layers support both properties and custom expressions for advanced configurations.
Interactions make it easy to explore and understand your map data by showing details about features. You can use pop-ups, which appear next to a selected or hovered feature, or info panels, which display detailed information on the right side of the map.
There are two primary types of interactions available:
Click Interactions: Retrieve information when you click on a feature in the map.
Hover Interactions: Display information when you hover over a feature in the map.
Non-aggregated layers
How it works: Information is retrieved directly from the server when you click on points, lines, or polygons.
Highlighting: Features are automatically highlighted when clicked.
Aggregated layers
How it works: Information is calculated on your computer for aggregated data (e.g., spatial indexes, cluster, etc.).
Aggregation operation: You’ll need to specify how data is aggregated (e.g., totals, averages).
Non-aggregated layers
How it works: Information is retrieved on your computer for individual points, lines, or polygons when you hover over them.
Highlighting: Requires a unique identifier column named geoid
to enable highlighting.
Limitations:
Up to 5 columns of information can be displayed.
Column character values are limited to 150.
Aggregated layers
How it works: Information is calculated on your computer for grouped data when you hover over it.
Aggregation operation: You’ll need to specify how data is aggregated (e.g., totals, averages).
Connections to Redshift clusters only support the aggregation of categorical properties by any value for styling and interactions.
You can use the menu to choose between a pop-up window (displayed next to the feature on the map) or an info panel (added to the right-side panel).
You can add attributes by including them as a list or customize pop-ups to meet your specific requirements using HTML. With HTML customization, you can add images, modify styles, and more to create tailored user experiences.
As an Editor user, you have the flexibility to determine wether SQL Parameters controls will be accessible in the public or shared version of the map. You can manage this from the "Map settings for viewers" section located at the top right of Builder interface.
Viewer users can then specify parameter values using the Parameters control interface found on the right panel. This enables them to adjust the underlying data according to their unique needs.
This setting will allow Viewer users to select values and re-run SQL queries.
Please be aware that in many cases this might produce a lot of different combinations of parameters that might not be cached in CARTO's CDN, and queries will reach the data warehouse and be executed, which might have a cost and performance implication.
The basemap selector, located below the zoom control, allows users to switch between available basemaps within their organization. This functionality enhances the visual context of maps, making them more adaptable to specific use cases and improving overall data exploration.
As an Editor, you have the flexibility to decide whether the basemap selector will be available in the public or shared versions of the map. This can be configured through the "Map settings for viewers," located at the top-right corner of the Builder interface.
Once enabled, Viewers can switch between basemaps to modify the map’s context according to their specific needs.
Select CARTO in the menu to see different basemap options. They include:
Voyager: Basemap with colors to clearly differentiate natural and cultural features.
Positron: Light basemap with dark-colored text.
Dark Matter: Dark basemap with light-colored text.
You can manage the CARTO basemap layers to hide and show water, buildings, roads, and more.
Select Google Maps in the menu to see different basemap options. They include:
Roadmap: Displays the default road map view.
Satellite: Displays Google Earth satellite images.
Hybrid: Displays satellite imagery with overlays of road names, city names, and other labels.
Terrain: Displays a physical map based on terrain information.
Positron, Voyager, and Dark Matter: New versions developed for Google Maps.
CARTO supports custom basemaps set up at the organization level by Admin users. When custom basemaps are available, they will be displayed under the Custom tab section of basemaps.
Numeric parameters are replaced by a single or a pair of numeric values in your query. They are a good choice if you need to filter your data by specific range or calculate analysis based on numeric values.
When creating a numeric parameter, there are a few settings to be defined:
Choose your preferred slider and scale type, be it simple or range for slider type, and discrete or continuous for scale, to optimize data collection according to your needs.
Simple Slider: Allows for the selection of a single numeric value.
Min Value: The smallest value a user can select.
Default Value: The initial value that is presented when the parameter is first used.
Max Value: The largest value a user can select.
Range Slider: Enables the selection of a pair of numeric values, defining a range.
Min Value: The smallest value within the range a user can select.
Max Value: The largest value within the range a user can select.
Scale: Determines the selection scope and type on a slider, which can be either continuous or discrete.
Continuous: Allows unrestricted selection of any value within a defined range.
Discrete: Permits selection from specific values within a range, controlled by a "Step Increment" that defines the interval between selectable values.
Display name: The name that will appear in the control UI.
SQL name(s): The name(s) to be used in the SQL query to be replaced by the control UI value(s).
Once the parameter is created, we should manually add it (using its SQL name) to one or more of your SQL Query data sources.
Simple Slider
After adding the parameter to your SQL query data sources, the control UI will appear in the right-side panel, allowing users to define a custom numeric value:
Range Slider
After adding the parameter to your SQL query data sources, the control UI will appear in the right-side panel, allowing users to define the min and max pair of numeric values:
In the Visualization section, you can easily identify the type of layer your visualizing. Additionally, within the Advanced visualization options , you can access Zoom Visibility to define the range at which your layer should be visualized. This ensures that your lines are displayed at appropriate zoom levels, improving the clarity and relevance of your map.
When configuring either the color based on a property, you can access Advanced fill options to set the color scale. This allows for a more granular and informative visualization.
When configuring either the stroke color or stroke weight based on a property, you can access Advanced stroke options to set the color scale or weight scale. This allows for a more granular and informative visualization.
When configuring the height based on a property, you can access Advanced height options to set the height scale. Additionally, you can enable the Show wireframe option to visualize the stroke of the 3D objects.
Within the Advanced Visualization Options , you can control the opacity of your raster layer, allowing you to blend it seamlessly with other layers on your map for improved visual clarity.
applies only to layers listed in the map layer list. If you want to allow exporting a layer’s data, ensure that the layer is included in the map layer list.
In the display section, you can also use formatting to choose the unit of metrics displayed on the y-axis. Additionally, you can add notes that support .
A basemap is a crucial component of any map, providing essential context, spatial features, and the visual foundation for your creations. CARTO Builder supports three different type of basemaps: , and .
Admin users can configure the basemap options available in Builder. The availability of basemap types depends on the organization-level configuration. For more details on configuring and managing basemaps, .
For Self-Hosted deployments, Google Maps API Key must be added to the configuration to enable the use of Google Maps within the organization. Learn more in this .
To learn more about how you can upload custom basemaps to the CARTO platform and the supported formats, check . For a step-by-step guide on custom basemaps, check out in the Academy.
These Artificial Intelligence Terms and Conditions (“AI Terms”) together with the CARTO Platform Terms and Conditions (“Agreement”) and the applicable Order Form between CartoDB Inc. (“CARTO”) and the customer, identified on the applicable Order Form (“Customer”), constitute the terms that govern the contractual relationship between the Customer and CARTO. Any undefined terms shall have the same definition as in the Agreement.
Acknowledgment and Acceptance. Customer may activate “CARTO IA Agent” at any time. CARTO AI Agent means the AI Model functionality available in the CARTO Platform. By enabling the 'CARTO AI Agent' feature in their CARTO Platform account, the Customer, through its Authorized Users, agrees to and accepts these AI Terms.
Machine learning technology. To provide the CARTO AI Agent feature and services, CARTO uses third party technology from OpenAI, in particular the OpenAI Assistant AP. For more information please refer to https://platform.openai.com/docs/assistants/overview.
Use of Customer Content.
Intellectual Property Rights. Customer Content may be used to provide AI Results (as defined below). Customer retains all right, title and interest in and to the Customer Content.
Use of Customer Content. Customer Content used by the CARTO AI Agent and therefore submitted via the OpenAI Assistant API, including queries sent through CARTO AI Agents will never be used to train, modify or improve AI Models. For the avoidance of doubt, the CARTO AI Agent does not retain, learn from, or adapt based on the Customer Content. "AI Models": means a computational system or algorithm designed to simulate intelligent behavior by processing data and generating outputs based on predefined or learned patterns. AI models may include machine learning, natural language processing, computer vision, and other artificial intelligence techniques. These models operate based on input data but do not inherently learn or adapt from that data unless explicitly programmed to do so.
Privacy and security.
Data Processing Addendum. CARTO’s Personal Data Processing Agreement (the “PDPA”), located at https://carto.com/legal/pdpa , is incorporated into and is made subject to these AI Terms. The PDPA applies to the Parties to the extent that Customer Content includes any Personal Data that is subject to the European Union’s General Data Protection Regulation 2016/679 (“GDPR”), or the GDPR as entered into UK law by virtue of the United Kingdom's European Union (Withdrawal) Act 2018 (“UK GDPR”). By using the Services, Customer agrees to the terms specified in the PDPA.
Sensitive Personal Data. Customer shall not cause CARTO to process Sensitive Personal Data without the prior written approval of CARTO, unless such Sensitive Personal Data has been previously to such upload converted into information which does not relate to an identified or identifiable person, or information that is rendered anonymous in such a way that a natural person is not or no longer identifiable (“Anonymous Data”).
HIPAA. Customer agrees not to use the Services to create, receive, maintain, transmit, or otherwise process any information that includes or constitutes “Protected Health Information”, as defined under the HIPAA Privacy Rule (45 C.F.R. Section 160.103).
Security. CARTO implements the security measures it deems necessary in accordance with the risk analysis carried out. OpenAI maintains strict security measures including industry recognized independent compliance assessment and reports such as SOC2 Type2 and CSA STAR Level 1, available at https://openai.com/security-and-privacy/.
AI Results and Intellectual Property.
AI Results. Customer may use CARTO AI Agents services to allow Authorized Users to interact with maps displaying Customer Content and extract insights through a conversational interface, each of the results of such interaction an “AI Result(s)”.
Intellectual Property of the AI Results. All intellectual property rights derived from the AI Results will be owned by Customer. Notwithstanding the foregoing, Customer acknowledges that AI Results are generated automatically by machine learning technology and may be similar to or the same as AI Results provided to other customers, and no rights to any AI Results generated, provided, or returned by the Service for or to other customers are granted to Customer under these AI Terms.
Restrictions. In addition to the limitations in the Agreement, Customer may not and will not permit others to:
use the CARTO AI Agent or Customer Content in a manner that violates any applicable laws or OpenAI Policies (as defined in the Open AI Terms and Conditions available at https://openai.com/policies/business-terms/);
use CARTO AI Agent in a manner that infringes, misappropriates or otherwise violates any third party’s rights;
use any personal information of children under 13 or the applicable age of digital consent or allow minors to use our OpenAI services without consent from their parent or guardian;
use AI Results to develop any artificial intelligence models that compete with OpenAI products and services.
use any method to extract data from the CARTO AI Agent other than as permitted through the APIs; or
buy, sell, or transfer API keys from, to or with a third party.
CARTO AI Agent limitations.
Customer acknowledges that there are numerous limitations that apply with respect to AI Results provided by large language and other AI Models due to the fact that it is automatically generated, including that (a) it may contain errors or misleading information, (b) AI Models are based on predefined rules and algorithms that lack the ability to think creatively and come up with new ideas and can result in repetitive or formulaic content, (c) AI Models can struggle with understanding the nuances of language, including slang, idioms, and cultural references, which can result in output that is out of context or does not make sense, (d) AI Models do not have emotions and cannot understand or convey emotions in the way humans can, which can result in output that lacks the empathy and emotion that humans are able to convey, (e) AI Models can perpetuate biases that are present in the data used to train them, which can result in output that is discriminatory or offensive, (f) AI Models can struggle with complex tasks that require reasoning, judgment and decision-making, (g) AI Models require large amounts of data to train and generate content, and the data used to train AI Models may be of poor quality or biased, which will negatively impact the accuracy and quality of the generated output, and (h) output can lack the personal touch that comes with content created by humans, which can make it seem cold and impersonal.
Customer agrees that it is responsible for evaluating, and bearing all risks associated with, the use of any content, including any reliance on the accuracy, completeness, or usefulness of AI Results.
DISCLAIMERS
“As Is” Basis. ANY AI RESULTS ARE PROVIDED “AS IS” “AS AVAILABLE,” “WITH ALL FAULTS” BASIS AND WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED.
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The Feature Selection tool allows you to apply a mask to the map, filtering out the features that lie outside of it. This filtering also affects the data showed in widgets, which turns the Feature Selection tool into a powerful device to quickly find insights in your geospatial data.
This guide describes how to start using this fantastic tool and get the most out of your maps.
You can explore this new tool by clicking on the button on the upper right corner of the map.
There are different selection modes supporting different kinds of shape. The mode selected by default is Polygon
.
Click on one of the selection modes to activate the tool and start drawing and filtering. The blue icon means that the tool is activated.
The selection tool operates on the visible layers on the map, and it is used by clicking and dragging to trace the vertices of a selection.
After tracing the shape of your mask, double click to stop drawing.
Once the mask has been drawn, it can be selected and modified by adding/removing/moving vertices or by moving the shape to a new location. To perform one of these actions, the shape has to be in edit mode by clicking on Edit Mask.
Once in editing mode, click on the mask and it will turn blue. The different vertices will also appear so that they can be modified or moved to a different location.
By default the mask is active but it can be disabled temporarily by clicking on Clear mask and re-enabled again by clicking on Apply mask. You can also click on the “X” to remove it permanently.
Some editing tips for working with the tool:
Add a vertex with each click.
Delete a vertex by clicking on an existing one.
To move a vertex, click and drag it.
To move your mask to another location, click on it and drag it around the map.
The following examples give a small demonstration of how to use the tool and how it easily allows you to explore your data and obtain information in a different way and in just a few steps.
CARTO Builder offers a UI that helps building SQL queries to perform geospatial analysis operations.
To get started with SQL Analysis, add a source to your Builder map and click on the three dots to find the Add SQL Analysis option:
After that, you will see a list of analyses compatible with your source. Compatibility depends on some factors, like whether or not the CARTO Analytics Toolbox is needed, or the connection’s cloud data warehouse.
Check this table to find out which analyses are available for each data warehouse:
Intersect and Aggregate
✅
✅
✅
✅
✅
Create buffers
✅
✅
✅
✅ (*)
✅
Add column from second source
✅
✅
✅
✅
✅
Filter by column value
✅
✅
✅
✅
✅
Calculate Centroids
✅
✅
✅
✅
✅
Clustering K-Means
✅ (*)
✅
✅ (*)
✅ (*)
✅
Trade Areas
✅ (*)
✅
✅ (*)
✅ (*)
(*) Requires the CARTO Analytics Toolbox to be installed
Each analysis will create a SQL query that performs the geospatial operation. These SQL queries will use CTEs (Common Table Expressions) to be able to chain different analyses and create a more complex sequence.
The resulting SQL queries from each analysis will take into account the syntax, specific functions and other nuances between different cloud data warehouses.
The resulting SQL query can be loaded in the map in different ways:
Run SQL analysis will load the query as a SQL Query source in Builder immediately.
Preview SQL analysis query will load the query in the SQL panel in Builder, so you will be able to review and modify the query before running it.
Save results in a new table will let you select the destination of a table that will contain the result of the query. The table will then be loaded as a source to the map.
The following are the currently available SQL analysis:
This analysis allows to perform a geospatial intersection between two different sources, aggregating data from the second source into the base one, when geometries from the second source intersect with geometries in the base source.
The result of this SQL analysis includes all columns from the base source and an extra one, called agg_value
, that contains the aggregated data from the features in the second source that intersect with each row in the base source.
Parameters
Second source: pick an existing source from your map, or a table from your cloud data warehouse to be used as the second source for this analysis
Aggregation operation: select the operation to aggregate the data from the second source.
Aggregation column: select the column from the second source that will be aggregated.
Example
A very common use case for this SQL Analysis would be “Get the average revenue from all stores in each neighborhood”. In this hypothetical scenario:
a table containing the polygon geometries for each neighborhood would be the base source.
a table containing the point geometries of each store would be the second source.
the aggregation operation would be the average (AVG
).
the aggregation column would be the one that contains the revenue in the stores table.
This analysis allows creating a LEFT JOIN
SQL query, allowing to include columns from both base and second source to be included in the result.
Parameters
Key columns: For each source, a key column needs to be selected. This column will be used to join the rows from the base and second source that share the same value.
Columns to be included in the result: Select the columns from each source that will be included in the result.
This analysis allows to keep or discard rows based on a column value.
Parameters
Target column: Select the column that we’ll be used for the filter.
Filter operator: Select a type of filter from the list of available operators.
Values: Use the selector to configure your filter.
This analysis creates a distance buffer around your existing geometries. It works with points, lines and polygons, and the resulting geometry will always be polygons.
Parameters
Distance: Select the distance that will be used to create the buffers.
Tracts: Select the number of concentric buffers that will be created.
Individual/Combined result: Select between having an individual buffer created for each row, or combine them all in a single polygon.
This analysis will produce a point that represent the centroid of the geometries in your source. By default, it will produce a single point. Using the Categorize optional parameter we can get a centroid per category in the dataset.
Parameters
Categorize: Select a column that contains categories to create one centroid per category in your dataset.
Aggregation: Aggregate data from the original dataset into the resulting centroids. The result of the analysis will include a column aggregated_value
that contains the value of the aggregation.
Aggregation Operation: Select an aggregation operation from the list.
Aggregation Column: Select a column to be aggregated.
This analysis uses the ST_CLUSTERKMEANS
function from the CARTO Analytics Toolbox for BigQuery, or the ST_ClusterKMeans()
in PostGIS, taking a set of points and finding a defined number of clusters based on the k-means algorithm. It generates a cluster_no
column that indicates the cluster that each point belongs to.
Parameters
Number of clusters: Define the number of clusters that will be produced by the analysis.
This analysis can be performed safely with up to ~700K rows. Bigger sources can cause the resulting SQL query to hit some limits BigQuery. Due to a Redshift internal limitation, the query produced by this analysis includes a LIMIT 100
clause. Removing this limit might cause an error on the query execution.
This analysis leverages the CREATE_ISOLINES
function in the CARTO Analytics Toolbox for BigQuery, Snowflake and Redshift to generate time or distance isolines based on different modes of transportation.
The input source for this analysis should contain point geometries that will be taken as the origin point for the isoline generation.
This SQL Analysis is available for BigQuery, Snowflake and Redshift connections, and it requires a specific minimum version of the CARTO Analytics Toolbox Advanced module to be installed:
Snowflake: 2022.06.09
Redshift: 2022.06.07
The result from this analysis can only be saved as a new table.
Parameters
Mode: Define the transportation mode that will be used for the isoline computation.
Range Type: Define the type of range that will be be used for the isoline computation:
Distance: The resulting isoline will describe the area that can be covered by traveling a specific distance set in meters.
Time: The resulting isoline will describe the area that can be covered by traveling during a specific time set in seconds.
SQL Parameters are placeholders that can be used on any SQL Query data source in Builder.
After creating a new SQL Parameter, it needs to be added manually to one or more SQL Query data sources.
Once added, the actual value for the parameter can be defined through a control UI in the right side panel's 'Parameters' tab. By selecting values from this UI, the placeholders in the SQL query will be dynamically replaced with the chosen values, enabling users to interactively customize the data displayed and analyzed in their maps.
SQL Parameters are categorized based on the data format of the values expected to be received, ensuring flexibility and ease of use. Below are the current type of SQL Parameters:
Date Parameter: Ideal for handling date values, date parameters allow users to input a specific date range, enabling data analysis over precise time periods. For example, analyzing sales data for a specific month or quarter.
Text Parameter: Tailored for text values, users can input or select a specific category to obtain precise insights. For instance, filtering Points of Interest (POI) types like "Supermarket" or "Restaurant".
Numeric Parameter: Designed for numeric values, users can input specific numerical criteria to filter data or perform analysis based on their preferences. For example, updating the radius size of a geofence to update an analysis result.
Please let us know if you need to leverage SQL parameters with other types of data.
Please note that SQL Parameters are not currently supported for pre-generated tileset or raster sources.
SQL Parameters are not currently supported for Databricks data sources.
SQL Parameters can be used in many different ways. One of the most common is allowing viewers to interact with the data in a controlled manner. Let's cover a simple use case step by step:
The option to create a new SQL Parameter will be available once there is at least one data source of type Query:
So, let's create a SQL Query data source with a table that contains information about fires all over the world:
On a new map, click on 'Add source from...' and select 'Custom query (SQL)' .
Select CARTO Data Warehouse as connection.
Use the following query
Create and configure a text parameter
Once we have the data rendered in the map, we'll add a text parameter that helps us select between fires that happened during the day or the night.
Click on 'Create a SQL Parameter'
Select 'Text Parameter'
In the 'Values' section, click on 'Add from source'. Select your data source and pick the daynight
column
In the 'Naming' section, pick a display name, like 'Day/Night'. The SQL name gets automatically generated as {{day_night}}
After the parameter has been created, open the SQL panel and add it to your query:
You can now use the control UI to add/remove values and check how the map changes.
Now, let's add a date parameter to filter fires by its date:
Click on 'Create a SQL parameter'
Select 'Date parameter'
Type or select from a calendar the range of dates that are going to be available from the control UI.
Give it a display name, like 'Date'. The SQL names gets automatically generated as {{date_from}}
and {{date_to}}
Open the SQL Panel and add the parameters to your query, like:
The parameters {{date_from}}
and {{date_to}}
will be replaced by the dates selected in the calendar.
Next, we'll incorporate a range slider to introduce a numeric parameter. It will allow users to focus on fires based on their brightness temperature to identify the most intense fires.
Click on 'Create a SQL parameter'
Select 'Numeric parameter'
In the 'Values' section, select Range Slider and enter the 'Min Value' and 'Max Value' within the range a user will be able to select.
Give it a display name, like 'Bright Temp'. The SQL names gets automatically generated as {{bright_temp_from}}
and {{bright_temp_to}}
Open the SQL Panel and add the parameters to your query, like:
Bonus track: Filter points and aggregate them into an H3 grid
In order to have a more effective visualization that help us identify areas of higher concentration of fires, we should aggregate the points into a grid that uses spatial indexes.
For that, we could just pick 'Quadbin' as a visualization type in the layer settings. Learn more about it here.
Another option would be to leverage H3 indexes using the functions from the Analytics Toolbox to create an aggregated grid from the points in our dataset. For that, open the SQL Panel and modify your query so it looks like:
Make sure to select H3 as geospatial type before running the query. After that, selecting different options in the parameters controls should trigger a dynamic recalculation of the aggregation.
We can use SUM(num_features)
to style our H3 grid, in order to detect areas with higher concentration of fires:
The map description feature enriches your map by adding contextual details. This makes it easier for all viewers, even those unfamiliar with the subject, to understand and utilize the map's information effectively.
This feature supports Markdown syntax, enabling you to incorporate headers, text formatting, links, images, and more. This added layer of customization enhances clarity and provides better insights into the displayed data, assumptions, and even guidelines for interpretation.
To add a description to your map, complete the following steps:
After clicking, Edit mode will activate, revealing a designated input area in the right panel. You can use Markdown syntax for advanced formatting options, including links and images.
Use the Preview mode to see how your description will appear to viewers.
Once you've added the description and updated the map, it becomes immediately available for all viewers upon publication.
To ensure the map description panel is the first thing viewers see when the map loads, publish the map with the description visible. If you don't, the Widgets/Parameters panel, if present, will display by default.
One way to find a location on a map is to search for it. For that, you can pan and zoom until you find what you want, but using specific tools for this purpose is often faster and more precise.
The Search Location bar present in the map offers a convenient method to search for locations using latitude longitude coordinates (lat,lon) or addresses and visualize them on the map.
To use the Search Location bar, complete the following steps:
Open a Builder map.
Click on the Search Location bar and type the complete address or coordinates (lat,lon) you want to search for.
Press Enter when you finish typing the location.
The map display zooms to the location of the search result and a blue marker at the specific location appears.
Tips to search by coordinates:
List latitude coordinates before longitude coordinates.
Check that the first number in your latitude coordinate is between -90 and 90.
Check that the first number in your longitude coordinate is between -180 and 180.
Format the coordinates using decimal degrees (e.g., 36.284065) instead of commas (e.g., 36,284065).
Focus on User's Device Location allows the map to automatically zoom in and center the view on your current location, providing you with a convenient and immediate view of your surroundings.
To find your location on the map, complete the following steps:
Open the Builder Map application you want to view.
Click the Focus on User's Device Location button.
The map will use your device's location services to determine your current position. Once your location is determined, the map view will automatically zoom in and center on your current position. You will see a blue icon indicating your location on the map.
Focus on User's Device Location finds your physical location from your web browser so please ensure that your device's location services are turned on.
You can follow the below steps to activate location settings for the most popular web browsers:
Google Chrome: In Chrome, users can enable or disable location services by going to Settings > Privacy and security > Site settings > Location. From there, users can turn location services on or off for all websites or specific websites.
Firefox: In Firefox, users can control location services by going to Preferences > Privacy & Security > Permissions > Location. From there, users can choose to block or allow location services for all websites or specific websites.
Safari: In Safari, users can control location services by going to Safari > Preferences > Websites > Location. From there, users can choose to allow or deny location services for all websites or specific websites.
Microsoft Edge: In Edge, users can control location services by going to Settings > Privacy, search, and services > Location. From there, users can turn location services on or off for all websites or specific websites.
Please note that the specific steps to access location settings may vary depending on the version of the browser and operating system being used.
This feature is currently in Public Preview for SaaS organizations. We're already working of our next version of faster, smarter and more powerful AI Agents for maps. Stay tuned!
CARTO AI Agents enhance user interactions with Builder maps by making data exploration intuitive and conversational. With AI Agents, users can extract insights and interact with maps seamlessly through a conversational interface.
Admin users can enable CARTO AI Agents for their organization by navigating to the AI Agents tab under Customizations in the organization settings. Once activated, Editor users can add an AI Agent to their Builder maps, which will be accessible in Viewer mode.
By enabling this feature, you confirm acceptance of the CARTO AI Terms and Conditions.
Once AI Agents are enabled at the organization level, Editors can activate them on individual Builder maps. To do this, navigate to the AI tab in the left panel of the Editor mode in Builder and toggle the option to enable the AI Agent. Once the Agent is created, you can interact with it as an Editor using the Preview mode or as a Viewer when you share your map.
Please note AI Agent is not supported in Public maps.
When the AI Agent is created or updated, the Agent is automatically provided with the map's configuration details—such as the sources, layer configuration, widgets, and tools available for end-users. This ensures the Agent can effectively understand and interact with the map without requiring manual input of these details.
Customizing the Agent is optional but highly recommended to enhance its functionality and align it with specific use cases. Editors can customize the Agent by adding:
Map context: Providing additional instructions and context specific to the map. For example, include business logic, style preferences, or any relevant information to improve the Agent’s responses.
Conversation starters: Predefining up to four prompts to guide user interactions and help users get started.
User guide: Including a brief introduction that explains the Agent’s functionality and how users can interact with it.
When accessing your map, the AI Agent icon is located at the bottom of the map interface. Clicking on it opens the conversational dialog, where users are greeted with a brief description and any predefined conversation starters.
During this interaction the Agent may temporarily control the map to perform tasks such as filtering, zooming, or switching layers.
CARTO AI Agents can provide both text-based insights and direct map interactions. Available tools include:
Search and zoom to locations: Easily identify and zoom to locations, including specific geographic extents like "Northern Portugal" or "Berlin city center."
Extract widget values: Analyze data conversationally by extracting widget values.
Filter data via widgets: Apply conversational filters to widgets, simplifying data exploration.
Switch layers on and off: Toggle layers directly from the conversation.
Retrieve maps coordinates: Access the latitude and longitude of your current map position.
Limitations
Extracting widget values is capped at 20 values for Category widgets and 50 values for Histogram and Time Series widgets.
Table Widget data extraction is not supported.
Filtering via Time Series widget or SQL parameters is not yet available.
Each organization has a monthly quota for CARTO AI Agent usage, which resets on the first day of each month. The quota includes:
Threads: Each organization can initiate up to 250 threads per month.
Tokens per thread: Each thread is limited to 250,000 tokens (words, punctuation, and formatting).
For more information about token limits, refer to OpenAI’s token usage guide.
CARTO AI Agents leverage the OpenAI APIs with the GPT-4-o model. Our plan is to integrate AI Agents with additional AI providers and models, further expanding their functionality and versatility.
Other future advancements will include:
Allow AI agent to query sources for richer insights, enabling real-time data retrieval and transforming raw geospatial data into actional insights without relying just on widgets.
By leveraging CARTO Workflows, users will be able to define analytical and data processing pipelines, empowering AI Agents to deliver richer and more meaningful results.
Extending AI Agent capabilities to include Public maps, broadening their applicability and accessibility.
This guide describes how to switch between different map view modes in Builder. There are two different map view modes, normal (flat) and 3D. The mode selected by default is the Normal map view.
To switch to 3D map view, click on 3D view from the “Map view” options.
Also, another cool functionality of CARTO Builder is the ability to have a dual map configuration to allow comparisons. We can switch to this mode by selecting Switch a dual map view from the “Map view” options.
You can always back to the single map configuration by clicking on Exit dual map view and selecting the panel you want to close, left or right.
Using Builder you can calculate point-to-point distances using the Measure tool. This tool is ideal to determine the direct distance between two locations on the map.
To use the Measure tool, complete the following steps:
Open a Builder map.
Click on the Measure tool to activate it.
Click on your starting point on the map, then continue to click along the line you wish to measure. You can add multiple nodes to trace the exact path or route.
To complete your measurement, either double-click on the final point or press Enter.
The tool will then display the total distance, allowing you to easily measure distances for a variety of applications.
If you need to measure something that extends beyond the current view of the map, like a long street, you can move the map while measuring. Simply press and hold the mouse button and drag the map to adjust your view. Continue your measurement seamlessly, and the tool will keep a running total of the distance. To finish, just double-click.
When managing Builder maps, you have two primary pathways: directly through the Builder interface or via the map card in the Maps section of your workspace.
Within these options, users may encounter two distinct actions, which may be available or restricted based on their assigned roles: duplicating maps and deleting maps.
To duplicate an existing map from the Workspace:
Navigate to the "Maps" section.
Click on the three-dotted menu in the map card.
Select "Duplicate Map".
To duplicate an existing map from the Builder interface:
Ensure your map has a title.
Click on the three dots.
Select "Duplicate Map".
If you click on the Duplicate map option, a dialog will appear warning you if any of the sources is not shared with you (eg: a private connection). Click on Yes, continue anyway button to confirm or click Cancel if you don’t want the map to be duplicated.
Note that if you duplicate a map with sources that are not shared with you, the private sources will load with errors and the corresponding layers will not be visible, leaving you the choice of fully removing them or asking for access. This ensures security across data sources, even when making editable map copies between users.
To delete a map from the Workspace:
Click on the three-dotted menu in the map card.
Select "Delete Map".
Confirm that you want to delete the selected map.
To delete a map from the Builder interface:
Click on the three-dotted menu in the top right of Builder.
Select the "Delete" quick action.
Confirm that you want to delete the selected map.
Note that if you delete a map that is shared (Organization or Public Map), other users will lose access to it and the public version of the map will become unavailable.
Editors can tag maps from the Workspace by editing their properties:
Click on the three dotted-button in the map card.
Select "Edit map properties".
Create, apply or remove existing tags in the tag input.
Additionally, you will be able to filter maps by tags using the tag filter in the workspace. Once a tag filter is applied, you will be able to copy the URL and share it with other users within your organization.
Tags are automatically deleted when they are no longer applied to any map or workflow.
Save a snapshot of your interactive Builder dashboard by downloading it as a PDF. This feature lets you export a screenshot of your map along with any necessary legends, the map description, widgets, and parameters. Once saved as a PDF, you can easily share the file offline, email it, or use it in any other way you utilize PDF files.
To ensure PDF reports remain manageable, table widgets included in the report are limited to displaying only the first 10 rows and visible columns. For full dataset access, use the Export Data functionality.
The "Download PDF report" button is located as a quick action next to the "Share" button at the top right corner of the Builder interface. It will be enabled as long as there are exportable data sources available in Builder.
How to download a PDF:
Click the 'Download PDF report' button. This action will capture the current view of the map, including the status of your widget and parameters you've applied.
In the download window, you have the option to add comments and decide whether to include the legend.
Click on 'Preview' to review how the PDF will look before you download it.
After previewing, you can go ahead and download the PDF if it meets your requirements.
To allow viewers of your map to download the interactive map as a PDF report:
Turn on 'Download PDF report', to enable viewers using the export viewport data functionality.
Publish your changes.
Viewers can export data following these steps:
In the download window, you have the option to add comments and decide whether to include the legend.
Click on 'Preview' to review how the PDF will look before you download it.
After previewing, you can go ahead and download the PDF if it meets your requirements.
Downloading PDF reports from Builder maps can be disabled entirely, for all users in your organization, for security purposes. If you're an admin and want to disable downloading maps as PDF, contact us through your usual point of contact or via email to support@carto.com.
The Map Preview feature is designed to provide an interactive experience of viewing and adjusting the map during the editing process. This ensures that all modifications are precisely reflected, allowing for a thorough review before making the map accessible to viewers.
Open the map you're currently editing or wish to review.
Find the "Preview" button, located in the top right of Builder interface.
Click on the "Preview" button to switch from editing mode to preview mode. This action allows you to view the map as an editor, with all current changes applied.
Preview mode is designed to complement the editing process, offering:
Publish updates : You can publish updates in Preview mode on the quick action or Sharing modal.
Modify sharing settings: Directly in Preview mode, adjust map permissions to control the map's accessibility.
Access sharing resources: Utilize resources in Preview mode to streamline the sharing process.
Seamless mode transition: Easily toggle between Preview and Edit modes to continue refining your map without losing any changes.
Apply adjustments: Make changes such as zooming and filtering in Preview mode. These adjustments are essential for reviewing how the map will appear to viewers and can be published directly from this mode.
Interactivity:
Navigation:
Scroll wheel zoom: Let users zoom in and out using the mouse scroll wheel.
Basemaps:
Basemap selector: Allow users to change the displayed basemap from the collection available at organization level.
Display:
Show title in public map: Display the title header in public or embed map.
Notifications:
Show performance warnings: Allow performance warning notifications in the application.
Looking for refresh sources functionality? We have moved this setting to the top right corner of Builder sources panel. Now you can set the data freshness according to your need as well as refreshing all sources at once directly from this new user interface.
With Builder, you can easily export your map data as CSV file(s). This functionality respects all the filters you set using the feature selection tool, widgets, or SQL parameters. The resulting CSV file includes detailed attributes and, if applicable, a geometry column.
The export viewport data button is located as a quick action next to the Share button, at the right top corner. It should be enabled as long as there are exportable data sources in Builder.
How to export:
Click the 'Export data' button. This considers the current viewport extent and any applied filters.
In the export window, select the layers you wish to export.
Each selected layer generates a job in the activity panel. You can monitor export progress here while continuing your work.
Once an export job is complete, click on the layer in the activity panel to download the CSV file(s).
Upon reviewal the output CSV file, you can visualize the detailed attributes included the geometry column if applicable. The output are the features within the viewport extent considering the filters if applicable for feature selection tool, widgets and parameters.
Bucket considerations
To allow viewers of your map to export data:
Turn on 'Export viewport data', to enable viewers using the export viewport data functionality.
Publish your changes.
Viewers can export data following these steps:
Choose the layer(s) to download from the modal window.
Each layer's export creates a job for tracking. Monitor the progress in the dedicated section.
Upon completion, click on the layer export job and download the CSV file(s).
Export limitations
There’s a limit of 8 layers per export process.
Pre-generated tileset and raster data sources don’t support export.
If you're using non-RDS for PostgreSQL data sources, keep in mind that export functionality isn't supported.
Exporting viewport data isn’t available for Databricks data sources.
Exporting viewport data is currently limited to Snowflake and Redshift data sources within AWS. This feature is not supported for Snowflake and Redshift hosted in GCP or Azure.
Exporting data applies only to layers listed in the Map Layer List. If you want to allow exporting a layer’s data, ensure that the layer is included in the Map Layer List.
Editors can share their maps with teammates or the public using Builder's sharing feature. To do so, click either the Share button on the top right of the map Builder, or select Share from the three-dot menu next to a map in the workspace. The available sharing options are:
Private: Only you can view and edit the map.
Organization: The map will be visible to users in your organization.
Entire Organization: The map will be visible to all users within your organization.
Specific users: The map will be visible only to specific users within your organization.
Public: Anyone with the map link can view it.
When the Specific users share option is selected, Editors can choose individual users to grant access to the map. These users will receive an email notification when the Share button is clicked, informing them that the map has been shared with them.
Access can be revoked at any time by clicking the three-dot menu next to a user's name and selecting Revoke access. From the same menu, Editors can also choose to resend the notification email.
Public maps can be set to require a password before they can be viewed. To enable this, simply tick the password protection checkbox in the share modal of a public map, set a new password and click Save This password can be changed or removed at any time.
Once this is set, anyone trying to access the map will be required to enter the password to view the map.
Once the map is accessible by others, you can access the following sharing resources:
Copy map link: Quickly share your map by copying its direct link. You can access this resource from the main Sharing modal or as a quick action.
Once you've shared your map with your organization, groups, or made it public, you can manage its updates to ensure that any ongoing edits enhance rather than disrupt the viewing experience.
To publish your recent modifications, use the "Publish updates" option. This is conveniently located as a quick action near the Share button or within the main sharing modal. This feature allows for swift publication of updates.
Upon publishing, the system ensures that all viewers access the most current version of the map. For editors, the modal will display the date of the most recent update, providing clear insight into the timeliness of the shared information.
Additionally, embedding a map can be a way to create beautiful data stories by wrapping your map with richer, interactive context and storytelling in a presentation or a website.
Maps that are shared as Public can be embedded anywhere without restrictions. Public maps protected with a password can also be embedded, with the additional password requirement.
You can control who can view your embedded map by sharing it exclusively with your Organization or with specific Groups. When you embed a non-public map, users need to meet the following requirements to view the map:
Users must be previously logged-in to CARTO in the same browser as the embedded map
Users must have access to the map (for example, they need to be part of the group that map is shared with)
If the user has access but is not currently logged-in to CARTO, they will need to login in a separate tab/window and refresh the page containing the embedded map.
The code for private embedding will soon be available directly in the sharing modal. In the meantime, just replace the src
URL in your iframe code with your shared map URL, using the /viewer/
path instead of /map/
or /builder/
. For example:
Users can interact with the embedded map in the same way they would in a standalone window by engaging with the map layers, widgets, SQL parameters...
By modifying dynamically URL parameters in your embedded map, you can, for example:
Control the map zoom and camera from user controls in your parent application
Modify the map filters (widgets and parameters) when the users selects different options in your parent application
Show/hide layers based on the user preferences in your parent application
The parent application must specify a valid origin
HTTP header for the CARTO embedded map to load. This is a security requirement.
For private embedding: If the user browser does not allow access to cookies to the CARTO embedded iframe, a screen will pop up, prompting for permission.
Some browsers such as Firefox or Brave offer advanced privacy/tracking protection that might interfere with the CARTO embedded iframe. While these features are great for user privacy, they also disable legitimate secure use cases like this one. Users can disable these protections.
You can collaborate with other users and work on the same maps. This is useful for publicly embedded maps where any teammate could fix or improve an issue, and also for joint research projects with other analysts.
To allow collaboration in your maps, open the Shared map settings and enable the Collaboration setting: "Allow other editors to edit this map"
After you enable this setting, all editors with access to this map will also be able to edit it. For example:
Collaboration is only possible if all the sources in the map use shared connections.
For security, private connections can't be used in collaborative maps. Otherwise, users in your organization could impersonate freely your database credentials.
Collaboration is asynchronous. If two users try to open the map at the same time, a pop-up will appear to decide who will take control of the map at that moment. If the current editor is away from the keyboard, the new editor can take control of the map in 30 seconds.
The current editor can also choose to deny the take over, remaining in control of the map until the map is closed.
URL parameters provide a versatile way to dynamically customize the presentation of your Builder map in viewer mode, enabling you to create tailored views for specific audiences without the need to create and manage multiple duplicated maps.
Use the '&' symbol to separate multiple parameters.
The first parameter should start with a '?' symbol.
Replace spaces in the URL with plus signs '+'.
Utilize encoded formatting, such as %28 and %29 for parentheses or %2C for commas, when needed.
Limit Considerations
URL Length: The URL of the map has a limit of 2083 characters. Exceeding this limit may result in the removal of parameters from the URL. Keep your URL within this limit for desired map settings.
Time Series Widget: URL parameters do not support the Time Series Widget settings.
Parameter Validation: Ensure correct parameter formatting and validity. Invalid or meaningless parameters will be ignored by the map.
Edit Mode: Parameters are only accepted when viewing the map either automatically or by manual input. Please note that any additions of parameters in the map URL editor mode will be ignored.
Refer to the table below for detailed information about each parameter that can be included in the URL to customize your map.
CARTO Workflows provides a visual language to design and execute multi-step spatial analytics procedures. With Workflows, our mission is to bring spatial analytics to a broader audience of data analysts and business users, and to democratize access to advanced Location Intelligence.
This tool, like the rest of the platform, is fully cloud native; running Spatial SQL in your own data warehouse, and leveraging CARTO’s Analytics Toolbox and the other components of our technology stack.
CARTO Workflows reduces the complexity and the high dependence on specialist knowledge. Users can leverage the scalability of cloud data warehouses through the use of spatial SQL without needing to write SQL code themselves. It opens up analytical modelling to all roles and skill levels, through a simple, familiar user interface.
Workflows runs in a directed graph structure, meaning that the workflow will run from left to right, step by step, but outputs of nodes can be used in the immediate next step or later in the Workflow. For example, if you have 5 nodes, the output of node 2 can connect to the immediate next node, or node 3, or nodes 4 or 5, or all if needed.
The CARTO team has designed this collection of Workflows examples with a hands-on approach to empower users and ease the Workflows learning curve.
These examples showcase a wide range of scenarios and applications: from simple building blocks for your geospatial analysis to more complex, industry-specific workflows tailored to facilitate running specific geospatial use-cases.
Making use of these examples is very easy. Just click on "New Workflow" and "From template" in your CARTO Workspace to access the collection of templates. Once the workflow is re-created you will be able to modifying as with any other workflow, replacing the data sources and re-configuring the different nodes so it can be useful for your specific use-case.
Before we jump into Workflows, let’s take a quick tour of the Workflows UI so you know your way around before getting started.
First is the Canvas where you will design your Workflow. This is a free-form Canvas meaning you can drag nodes onto any part of the canvas. You can zoom in and out to see different parts of your workflow and see the layout of the workflow in the mini viewer in the lower right corner. As you add nodes to the canvas they will snap to a grid to align.
On the left side, you will find a menu where you can add data sources from the connection you created the Workflow. You can add any data source that you want that exists in your connection. You also have all the components, or nodes, that you can add to the canvas. We will go over the various components later. You can search for components or scroll to find the component you want.
The bottom panel is the results space where you will see four different tabs:
Messages: Messages about the status of your Workflow including success and error messages.
Data: After clicking on a node, you can see the tabular data outputs of that specific workflow step.
Map: After clicking on a node, if that step returns a valid geometry, it will show up in the map. If there is more than one geometry you will have an option to pick which one to show.
SQL: The compiled SQL of the workflow. This includes different steps and procedural language.
This is the area of the interface where you will be adding the different components and data sources that are required to run your analysis, and connecting them in order to define each step of the workflow in order of execution.
In this central panel you will always find the main diagram of your workflow, with all its components and data sources connected according to what you have established.
In the workflow canvas you have the possibility to add visual assets and annotations to provide explanations on your workflow.
You can do it by clicking on the corresponding icon in the toolbar and drag on the canvas. This will help you make very complex workflows more understandable.
Clicking this button will let you select a location in the canvas to place a datasource. After that, select a local file from your computer or specify a URL that will be imported as a data node in your workflow.
Clicking this button will let you select a location in the canvas that will contain an automatic description of the workflow.
This description is obtained using Generative AI on the workflow's SQL code.
Double click on a node's name to add a custom name to it. By using more descriptive names, your workflow will be much easier to understand.
CARTO Builder strives to load data in the most efficient format for optimal visualization performance. Depending on the size of the data source, different mechanisms and performance recommendations are applied:
For all SQL queries, spatial index source types and datasets bigger than the limits in the chart above, data is loaded progressively as vector tiles, a method named Dynamic Tiling. These tiles will be dynamically generated via SQL queries pushed down to your data warehouse and rendered client-side as you pan the map.
When using dynamic tiling, large-scale data visualization is optimized by adjusting the display of different geometry types. For point geometries, fewer points are shown at higher zoom levels. For lines and polygons, features are simplified or selectively rendered based on zoom level. This approach ensures efficient, scalable, and clear map performance.
Response times depend on table size, geometry complexity, zoom level, and data structure. Indexing, clustering, or partitions can enhance query performance.
There are optimizations that can be applied to a table to improve query performance and reduce processing cost:
If your source contains simple features, you should cluster your table by the geometry column to ensure that data is structured in a way that is fast to access:
For spatial index sources, you must cluster the tables by the column containing the spatial index, as per this example:
When working with simple features, use ST_GEOHASH(geom)
to order your table:
Also, take into account that your Snowflake role must have been granted the SEARCH OPTIMIZATION
privilege on the relevant schema:
If you are working with spatial indexes, clustering the tables by the column containing the spatial index:
CARTO supports simple features in tables with a couple of requirements in order to be able to make fast geospatial queries to generate tiles dynamically:
Each row must contain four additional columns __carto_xmin
, __carto_ymin
, __carto_xmax
, __carto_ymax
that describe the Bounding Box of each feature. These columns help store the table in a way that allow fast queries and avoid full scans on each query.
This is an example query that uses Databricks Spatial SQL (running on Photon) to prepare a table with these requirements. In this example, the geom
column contains features as WKT strings.
If your original table already contains geographies as WKB binary, the query could be a bit simpler:
Your Databricks workspace needs to be enabled with Spatial SQL functions, which are currently in Private Preview.
When working with h3 spatial index, you should optimizie the table using ZORDER BY
expression, like:
And use the index to cluster the table:
Remember that the cluster needs to be recreated if the data changes.
Creating an index and using it to cluster the table:
or
and use the index to cluster the table:
Remember that the cluster needs to be recreated if the data changes.
If working with spatial indexes, you can use the SORTKEY
:
When the dynamic tile generation is not an option due to the table size, the complexity of the geometries, or any other of the possible caveats mentioned before, the best option to achieve a performant visualization is to generate a tileset.
Generating a tileset basically means that the table (or SQL query) will be pre-processed and a new table containing all the tiles for a selected zoom range will produced. This method is great for visualization of large volumes of data.
Find the map description icon located at the top-right corner of the header .
Locate the Search Location bar positioned at the top left corner of the map .
Locate the Focus on User's Device Location button positioned in the lower left corner of the map .
Locate the Measure tool icon located at the top of the map interface .
Navigate to the 'Map settings for viewers' button located next to the 'Preview' button.
Click the Download PDF report iconlocated at the top left corner. If Export viewport data functionality is also available, this icon allows you to access the Download PDF report option.
The map settings for viewers allows you to control different aspects of the application when accessed by viewers. This includes settings for interactivity, navigation, display options, and notifications. To access these settings, simply click the icon located next to the 'Preview' on the top right of Builder's interface.
Feature selection tool: Enable this for user-driven data filtering with rectangle, circle, or custom polygon tools. .
SQL parameters controls: This feature allows users to dynamically update data sources using parameter controls. .
Export viewport data: Allow users to export data from the viewport extent as CSV format. .
Search location bar: The search location bar allow viewers to quickly find places using addresses or coordinates. .
Measure tool: Activate this to allow users measure point-to-point distances on the map. .
Focus map on the current location: Allow users to center the map on their current location.
Check the new page to learn more.
For SaaS, CARTO automatically manages buckets and data exports. Be aware that for exports from Snowflake and Redshift sources, the AWS cluster needs to match the organization's CARTO SaaS region. Check your CARTO SaaS region in .
For Self-Hosted deployments, a bucket owned by the customer needs to be configured. Please refer to for more information.
Navigate to the 'Map settings for viewers' button located next to the 'Preview' button.
Click the export icon located at the top left corner.
For RDS for PostgreSQL data sources, make sure you have the set up, as it's necessary for export.
Exporting data from Builder maps can be disabled entirely, for all users in your organization, for security purposes. If you're an admin and want to disable exporting data, contact us through your usual point of contact or via email to .
Specific groups: The map will be visible only to specific groups of users in your organization ( for more information).
The Public map sharing option can be disabled for all users within your organization. If you're an Admin and want to disable this, please contact us through your usual point of contact or via email to .
Once a map is shared, Editors can enable to let other Editors edit the map, and they can set specific to direct Viewers to specific map views, layers, or geographic areas.
Embed this map: Seamlessly integrate your map into websites or applications with the provided embed code snippet available in the Sharing modal. .
Develop a custom app: Use the Map ID available in the Sharing modal to craft a custom application.
We've updated the location of the map settings for viewers! Now, you can find these settings directly under the icon, positioned next to the "Preview" button. This change streamlines your workflow by placing viewer-specific map settings outside the Sharing modal, making them more accessible. Discover all you need to know about adjusting map settings for your viewers .
Maps built with CARTO can be easily embedded in other websites or applications, using an . This is a great way for your maps to make a larger impact by reaching a larger audience, both outside and inside your organization.
For an in-depth tutorial on creating and embedding maps, we highly recommend visiting the , which contains practical step-by-step resources.
To embed a map, simply click on "Share" as seen in , and copy the embed code available in the sharing dialog. The resulting code should be similar to this one.
CARTO needs to be able to access cookies in the user browser. Learn more in the section of this page.
An additional way to interact with the embedded map is by modifying the . This is particularly interesting because it allows the parent application to control the state of the embedded CARTO map.
Clicks inside the embedded map (such as filtering or zooming) can't be tracked by the parent application. If you're interested in this use case, please .
If the map is (or if it's public) then all editors in your organization will be able to edit the map
If the map is "Managers" then only editors that also belong to the group "Managers" will be able to edit the map.
When interacting with the viewer mode of a Builder map, any changes made are automatically reflected in the URL. For instance, if you use the to find a specific address, the location will automatically be appended to the URL parameter, as shown in this example:
You can manually add URL parameters to customize the behavior of your map in viewer mode. To manually include URL parameters, follow these guidelines, along with the :
In order to improve performance and be able to inspect the results of intermediate steps, Workflows makes use of temporary data that, by default, is stored in a workflows_temp
schema/dataset in your data warehouse (learn more about temporary tables ).
The notes support , allowing to add different levels of headers, text formatting, code blocks, in line monospace code, bulleted and ordered lists, links, images, etc.
This feature has been deprecated in favor of the component. Take a look at the announcement in our section
On the toolbar on top of the canvas you will find the Import file into workflow button.
This feature is in public beta, available on demand. Please if you wish to have it enabled in your account.
On the toolbar on top of the canvas you will find the Generate workflow description button.
: These include simple features, SQL query sources, and spatial indexes. Data is loaded progressively as vector tiles generated dynamically via SQL queries to your data warehouse, optimizing performance while maintaining responsiveness.
: For large datasets, tilesets are pre-generated to handle high data volumes efficiently. This method is ideal for complex geometries or extensive datasets, ensuring high-performance visualizations.
These optimizations can be applied via the or manually from your Data Warehouse console or SQL clients
Check out for more information.
Activate (only available in Snowflake ) explicitly for the GEO index on the GEOGRAPHY column:
The geo column must be of binary type and contains a representation of the geography.
The Databricks team has made available to request access to the functions. Please get in touch with them through the form to gain access to all Spatial SQL functions.
will also help with performance. For example:
To avoid intermediate transformations, geometries should to be projected into EPSG:3857
and make sure that the SRID is set for the column. Take a look at the and functions reference.
For optimal performance, geometries need to be projected into EPSG:4326
and make sure that the SRID is set for the column. Take a look at the and functions reference.
You can create a pre-generated tilesets using or CARTO Analytics Toolbox for the different cloud data warehouse as below:
Tilesets in
Tilesets in
Tilesets in
Tilesets in
Tilesets in
lat
/ lng
Sets the center of the map.
Syntax rules:
lat
specifies the latitude
lng
specifies the longitude of the map's center.
Values should be in decimal degrees
Example:
lat=27.731667&lng=-81.346962
zoom
Defines the zoom level of the map, controlling the scale of the view. Higher values zoom in closer to the surface.
Example:
zoom=10
mask
Defines a geographic area to filter map layers and widgets using a polygon.
Syntax rules:
Must be in WKT POLYGON format.
Use %28
and %29
for parentheses.
Use %2C
for commas.
+
for spaces between coordinates.
Example:
mask=POLYGON+%28%28-2.601981+51.452270%2C+-2.601877+51.450410%2C+-2.594073+51.450319%2C+-2.594632+51.452358%2C+-2.601981+51.452270%29%29
search
Allows searching for a coordinate or address. When used, it centers the map on the specified location, updates the Search location bar, and places a marker icon at the location.
Syntax rules:
Coordinates must be in decimal degrees format.
Use +
to replace spaces within an address or between coordinate values.
Use %2C
to separate latitude and longitude values in coordinates.
Examples:
Address: search=broadway+new+york
Coordinates: search=40.790886%2C+-73.974709
widget_[widget_id]
Applies filters to widgets in the map. The unique widget_id
can be obtained using the "Copy ID" functionality available on each widget on Editor mode.
Syntax rule:
Replace [widget_id]
with the widget's actual ID.
Use +
to indicate spaces within concatenated filter values.
Use %2C
to separate different filter ranges for Histogram widget.
Time Series widgets is not supported by URL parameters.
Example:
Pie Widget: widget_5c7eb73e-5e2f-4639-90af-1b82b80a1969=Department+Store
Histogram: widget_6a1a3172-1bc2-49be-89a8-c1b008f4e43e=1413506.6-1526970.5
param_[sql_name]
Update the input values in SQL Parameter Controls updating the parameters applied in your SQL query sources.
Syntax rules:
Substitute [sql_name]
with the actual name SQL name of the parameter without brackets.
Encode spaces within text values with +
.
Format date values as YYYY-MM-DD
.
Encode dashes -
in numeric ranges with %2D
for specifying value intervals.
Example:
Date Parameter: param_event_date_from=2017-01-03¶m_event_date_to=2017-01-11
Numeric Parameter param_accident_influence_radius=70
Text Parameter: param_route_type=Popular+Busier+Roads
layers
Specify which layers are visible in the map.
Syntax rule:
List the indices of the visible layers separated by commas without spaces.
Each index corresponds to a layer's position in the Builder, starting from 0
.
Example:
layers=0,1
pitch
Adjusts the vertical angle of the map view in degrees, enhancing the perspective or 3D visualization effect.
Syntax rule:
The value can range from 0
(orthogonal view, directly above) to 60
degrees (tilted view, providing depth and perspective).
Example:
pitch=35
bearing
Adjusts the horizontal angle or bearing of the map view, specifying the orientation with respect to the cardinal directions.
Syntax rule:
The value is provided in degrees, measured clockwise, with North as 0
degrees, East as 90
degrees, South as 180
degrees, and West as 270
degrees.
Example:
bearing=180
Components to aggregate or disaggregate properties in your data in different ways.
Description
This component creates a new table with a single row and a 'count' column containing the row count of the input table
Inputs
Source table [Table]
Outputs
Result table [Table]
External links
Description
This component creates a new table with aggregated values.
It is required to convert spatial types to an allowed type by using 'ST_ASBINARY' before grouping by them.
Inputs
Source table [Table]
Columns and methods for aggregation [String]
Columns to group by [String]
Outputs
Result table [Table]
Description
This component adds new columns with statistics computed from the points of a second table that fall within each polygon.
Inputs
Points table [Table]
Polygons table [Table]
Geo column in points table [Column]
Geo column in polygons table [Column]
Column to compute stats for in points table [Column]
Column with identifier in polygons table [Column]
Outputs
Result table [Table]
Description
This component explodes multi-part geographies into multiple single-part ones.
The output table has the same structure as the input one, but the geo column only contains single part geographies. Rows with multi-part geographies are replaced by several rows with single-part geographies and identical values for the rest of columns.
Inputs
Source table [Table]
Geo column [Column]
Outputs
Result table [Table]
External links
This component creates a new table with aggregated values for the input whole table.
Inputs
Source table [Table]
Aggregation
: Use the UI of this component to select a list of aggregated properties that will be added to the result table.
Outputs
Result table [Table]
Description
This component unnests the arrays contained an a column.
Inputs
Source table [Table]
Column [Column]
Outputs
Result table [Table]
External links
The Results panel in Workflows provide information about each node in your workflow. Click on a node to open the panel and see its different sections.
The header of the panel contains general information about the workflow, like the time it was last executed or the connection being used. It also allows expanding to full screen or hiding the panel to leave more space for the canvas.
This tab contains information about the execution of the workflow. Error messages and confirmation for successful runs will appear here.
This tab contains a table visualization of the result of the node's execution.
For numeric columns, max, min, average and sum are displayed.
For string and date or timestamp columns, the frequency of the top 20 categories is calculated and shown.
The data tab also allows to explore the result in pages of different length, which can be configured in the bottom right corner of the panel:
Bucket considerations
For SaaS, CARTO automatically manages buckets and data exports. Be aware that for exports from Snowflake and Redshift sources, the AWS cluster needs to match the organization's CARTO SaaS region. Check your CARTO SaaS region in Settings.
For Self-Hosted deployments, a bucket owned by the customer needs to be configured. Please refer to this documentation for more information.
The Map tab shows a preview of the result on a map. It also allows to create a map in Builder using the selected node as data source. When there are more than one columns that could be rendered (several geometry/geography columns, spatial indexes, etc), you can use a selector to make sure the correct column is used in the Builder map.
This tab contains the SQL code that is executed in the data warehouse when the workflow runs from the UI, by clicking on the Run button.
Part of this code handles the creation of temporal tables and other control strategies. These portion of the code is collapsed and hidden by default, but it can be expanded by clicking on the small two dots inline with the code.
The code displayed in the SQL tab corresponds exactly with what is executed in your data warehouse by clicking the 'Run' button in the Workflows UI.
However, this code is different to the one exported or executed via API. In these cases, all the control code is ommitted and CTEs are used instead of temporary tables.
Components in CARTO Workflows are, together with the data sources, the available resources to build your analytical workflows. To use these elements, drag and drop them from the Components Explorer to the Workflow canvas.
In this section you can find the list of all components in CARTO Workflows and their availability in the different cloud data warehouse platforms. Please check this page regularly as currently we are continuously incorporating new components and also completing their availability across platforms.
This section also contains information about Extension Packages, which extend Workflows' functionality for specific use cases.
Extension Packages can be created for workflows that use BigQuery and Snowflake connections.
In addition to the extensions created by external developers, partners and other third-parties, CARTO provides a collection of extension packages developed and maintained internally. These are described below, and are distributed via .zip files that can be installed from the Workflows UI.
Please note that the components available via your CARTO Data Warehouse connection are the same ones as for Google BigQuery.
(coming soon)
(coming soon)
(coming soon)
Workflows' functionality can be extended and tailored to specific use cases, by creating custom components, which can be grouped and distributed together as Extension Packages.
Extension Packages can be created for workflows that use BigQuery and Snowflake connections.
In addition to the extensions created by external developers, partners and other third-parties, CARTO provides a collection of extension packages developed and maintained internally. These are described below, and are distributed via .zip files that can be installed from the Workflows UI.
Next to each column name, you will find this icon . Click on it to show some column statistics on the right side of the panel:
The copy button in the top right corner will copy the content of the current page to the clipboard, using tabs as delimiter, which makes it easy to paste directly into a spreadsheet.
The export button will start the export process of the data on the current node. The export will happen asynchronously, and the status will be reported in the activity panel in the top bar. Once finished, the resulting link(s) will be available.
l