Activity Data Examples
This is a collection of examples to inspire and guide you when analyzing your CARTO Activity Data, so that you can create your own analysis and KPIs in your tool of choice.
Example using a BI Tool: beginner-oriented example, where you can start surfacing insights from CARTO easily without coding, using a tool like Looker Studio.
Examples using SQL: advanced examples, where we show SQL snippets and patterns that highlight how to get the most from your CARTO Activity Data.
Requirements
Exporting Activity Data
Before following the exemples, you'll need to export your Activity Data. Learn how to export it with the following resources:
Example using a BI Tool
We can easily analyze CARTO Activity Data using Business Intelligence (BI) tools such as Looker, Tableau, PowerBI or Thoughtspot. For the following example, we'll use Looker Studio, but the steps should be similar in any other BI tool.
Uploading the data to Looker Studio
Open a new dashboard in Looker Studio and click on "Add Data" > "File Upload". Select your files and wait until they've been uploaded and processed.
Different tools use different rules for CSV validation. If Looker Studio shows errors with the CSV upload, you can try to fix the errors or contact our Support team for assistance.
Click on "Add to Report" and you should already see a basic placeholder chart with the number of events per day.
Creating a maps created over time chart
To analyze the number of maps created, we need to isolate the events with type = MapCreated. There are several ways to do this in Looker Studio, but an easy way is to create a filter like this:
Once you create the filter, it can be used in any chart, for example a Time Series Chart with the following configuration:
Dimensions:
ts
Metrics:
Record count
And that's it! The time series chart already showcases the amount of maps created per day, for the Activity Data that you exported. You can play with different options to create aggregations by week, day, etc.
Next step: maps created per user over time
You already know from these steps how to analyze Activity Data based on the type of events. But each event contains meaningful information in the payload column (JSON format), such as the user that triggered the event. For a complete reference of event types and payloads, check Activity Data Reference.
While there are ways to analyze the payload column directly in a BI tool we recommend processing the payload column using SQL for further flexibility, as shown in this example.
Creating a usage quota by user chart
Understanding which user consumes the most usage quota is an important KPI for organizations rolling out CARTO to different teams, and it's quite easy to obtain from the CARTO Activity Data.
First, we need to create a calculated field to understand the final usage quota. This is done by multiplying the amount
of requests by their relative usage_quota_weight
, as seen in the Activity Data reference.
Once we have that field, we can create a simple horizontal bar chart using the following configuration:
Dimensions:
user
Metrics:
final_usage_quota_consumed
Simple, right? We can tell already who is consuming the most usage quota.
Understanding who is who
The CARTO Activity Data tracks users with IDs, so that the data can remain anonymous when removing the link to the user. This happens, for example, when a user is deleted.
To translate user IDs into actual user emails you can use the complementary user list table in your Activity Data export.
Additionally, remember that public
means that CARTO can't tell who the end user is, such as an unauthenticated visitor in a public map, or a custom application using your own login.
Examples using SQL
In most cases you will be adding the CARTO Activity Data files (CSV or Parquet) to your company data catalog or data lake. Typically these files are stored in SQL-compatible databases and data warehouses, such as Google BigQuery or Snowflake.
The schemas of the CARTO Activity Data tables are very simple and are fully compatible with SQL databases, so let's build a few examples.
The following examples use the BigQuery SQL dialect, please consider the SQL dialect and functions available in your own database.
SQL: Maps created per user over time
This query returns a very simple table with the amount of maps that each user has created over time. You could join the userid
with the User List table to obtain the actual email of each userid
.
SQL: Workflow components used by each workflow and user
This query is centered around monitoring the use of Workflow components. It returns a list of the components that were used by each user and workflow execution over time. You could later aggregate this data to find out what components are used most frequently, or which users are using a specific component.
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