> For the complete documentation index, see [llms.txt](https://docs.carto.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.carto.com/data-and-analysis/analytics-toolbox-for-bigquery/guides/running-queries-from-builder.md).

# Running queries from Builder

Once you have created your BigQuery connection in the CARTO Workspace (see [*Getting Access*](/data-and-analysis/analytics-toolbox-for-bigquery/getting-access.md#access-from-carto-workspace) for details), you can create custom SQL layers in Builder that make use of the Analytics Toolbox for BigQuery.

{% hint style="info" %}
**From the CARTO Data Warehouse connection**

You can also run queries from Builder using the Analytics Toolbox for BigQuery from your CARTO Data Warehouse connection. This connection is available and ready-to-use for all CARTO accounts.
{% endhint %}

The Analytics Toolbox functions are available from the `carto-un` and `carto-un-eu` BigQuery projects. These projects are deployed in the US and EU multi-regions, respectively, and you may choose one or the other depending on the location of your data.

To get started, let’s run a simple example query to cluster a set of points using the [`ST_CLUSTERKMEANS`](/data-and-analysis/analytics-toolbox-for-redshift/sql-reference/clustering.md#st_clusterkmeans) function.

1. Click on the *Add source from* button in Builder, that can be found at the bottom left of the screen.

   <figure><img src="/files/za1U6JZ8el7oPcvjLt9l" alt=""><figcaption></figcaption></figure>
2. Select the second tab Custom Query (SQL) and pick the BigQuery or CARTO Data Warehouse connection that you will use to run the query.

   <figure><img src="/files/fiY6hWqZGQYfN3FIHW9D" alt=""><figcaption></figcaption></figure>
3. Click on Add source. A SQL console will be displayed.

   <figure><img src="/files/v62jUvkFlfhn0lke0r75" alt=""><figcaption></figcaption></figure>
4. Copy and paste the following query:

{% tabs %}
{% tab title="carto-un" %}

```sql
with clustered_points AS
(
    SELECT `carto-un`.carto.ST_CLUSTERKMEANS(ARRAY_AGG(geom ignore nulls), 6) AS cluster_arr
    FROM carto-demo-data.demo_tables.sample_customer_home_locations
)
SELECT cluster_element.cluster, cluster_element.geom AS geom FROM clustered_points, UNNEST(cluster_arr) AS cluster_element;
```

{% endtab %}

{% tab title="carto-un-eu" %}

```sql
with clustered_points AS
(
    SELECT `carto-un-eu`.carto.ST_CLUSTERKMEANS(ARRAY_AGG(geom ignore nulls), 6) AS cluster_arr
    FROM carto-demo-data.demo_tables.sample_customer_home_locations
)
SELECT cluster_element.cluster, cluster_element.geom AS geom FROM clustered_points, UNNEST(cluster_arr) AS cluster_element;
```

{% endtab %}

{% tab title="manual" %}

```sql
with clustered_points AS
(
    SELECT carto.ST_CLUSTERKMEANS(ARRAY_AGG(geom ignore nulls), 6) AS cluster_arr
    FROM carto-demo-data.demo_tables.sample_customer_home_locations
)
SELECT cluster_element.cluster, cluster_element.geom AS geom FROM clustered_points, UNNEST(cluster_arr) AS cluster_element;
```

{% endtab %}
{% endtabs %}

1. Run the query. This query computes five clusters from the points of the sample\_customer\_home\_locations table. As a result, each point is assigned a cluster ID.
2. Style the layer by the cluster attribute.

<figure><img src="/files/QTZuNLZNZ9G7njvPjyW8" alt=""><figcaption></figcaption></figure>

{% hint style="info" %}
For more examples, visit the [Examples](https://academy.carto.com/advanced-spatial-analytics/spatial-analytics-for-bigquery/step-by-step-tutorials) section in our Academy or try executing any of the queries included in every function definition in the [SQL Reference](/data-and-analysis/analytics-toolbox-for-postgresql/sql-reference.md).
{% endhint %}

<img src="/files/4m1BK9j4Wq34gat4HHd2" alt="EU flag" data-size="line"> This project has received funding from the [European Union’s Horizon 2020](https://ec.europa.eu/programmes/horizon2020/en) research and innovation programme under grant agreement No 960401.


---

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