--- title: Connect Databricks for Warehouse-Native Experiment Analysis description: >- Connect a Databricks service account to enable warehouse-native experiment analysis. breadcrumbs: >- Docs > Experiments > Experiments Guides > Connect Databricks for Warehouse-Native Experiment Analysis --- # Connect Databricks for Warehouse-Native Experiment Analysis {% callout %} # Important note for users on the following Datadog sites: app.ddog-gov.com {% alert level="danger" %} This product is not supported for your selected [Datadog site](https://docs.datadoghq.com/getting_started/site.md). (). {% /alert %} {% /callout %} ## Overview{% #overview %} Warehouse-native experiment analysis lets you run statistical computations directly in your data warehouse. To set this up for Databricks, connect a Databricks service account to Datadog and configure your experiment settings. This guide covers: - Granting permissions to the service principal - Connecting Databricks to Datadog - Configuring experiment settings in Datadog ## Prerequisites{% #prerequisites %} Datadog Experiments connects to Databricks through the [Datadog Databricks integration](https://docs.datadoghq.com/integrations/databricks.md?tab=useaserviceprincipalforoauth). If you already have a Databricks integration configured for the workspace you plan to use, skip to Step 1. Otherwise, expand the section below to create a service principal. {% collapsible-section %} #### Create a Databricks service principal **In your Databricks Workspace**: 1. Click your profile in the top right corner and select **Settings**. 1. In the **Settings** menu, click **Identity and access**. 1. On the **Service principals** row, click **Manage**, then: 1. Click **Add service principal**, then **Add new**. 1. Enter a service principal name and click **Add**. 1. Click the name of the new service principal to open its details page. 1. Select the **Permissions** tab, then: 1. Click **Grant access**. 1. Under **User, Group or Service Principal**, enter the service principal name. 1. Using the **Permission** dropdown, select **Manage**. 1. Click **Save**. 1. Select the **Secrets** tab, then: 1. Click **Generate secret**. 1. Set the **Lifetime (days)** value to the maximum allowed (for example, 730). 1. Click **Generate**. 1. Note your **Secret** and **Client ID**. 1. Click **Done**. 1. In the **Settings** menu, click **Identity and access**. 1. On the **Groups** row, click **Manage**, then: 1. Click **admins**, then **Add members**. 1. Enter the service principal name and click **Add**. After you create the service principal, continue to Step 1 to grant the required permissions. {% /collapsible-section %} {% alert level="info" %} If you plan to use other warehouse observability functionality in Datadog, see [Datadog's Databricks integration documentation](https://docs.datadoghq.com/integrations/databricks.md) to determine which resources to enable. {% /alert %} ## Step 1: Grant permissions to the service principal{% #step-1-grant-permissions-to-the-service-principal %} {% alert level="info" %} You must be an account admin to grant these permissions. {% /alert %} In your Databricks Workspace, open the **SQL Editor** to run the following commands and grant the service principal permissions for warehouse-native experiment analysis. {% image source="https://docs.dd-static.net/images/product_analytics/experiment/guide/databricks_experiments_sql_editor.bc7d2a668f0d5e2c9e13007b345508df.png?auto=format&fit=max&w=850 1x, https://docs.dd-static.net/images/product_analytics/experiment/guide/databricks_experiments_sql_editor.bc7d2a668f0d5e2c9e13007b345508df.png?auto=format&fit=max&w=850&dpr=2 2x" alt="The Databricks Workspace with SQL Editor highlighted in the left navigation under the SQL section, Queries listed below it, a New Query tab open with the New SQL editor: ON toggle at the top, an empty query editor, and a Run all (1000) button with a dropdown arrow." /%} ### Grant read access to source tables{% #grant-read-access-to-source-tables %} Grant the service principal read access to the tables containing your experiment metrics. Run both `GRANT USE` commands, then run the `GRANT SELECT` option that matches your access needs. Replace ``, ``, ``, and `` with the appropriate values. ```sql GRANT USE CATALOG ON CATALOG TO ``; GRANT USE SCHEMA ON SCHEMA . TO ``; -- Option 1: Give read access to a single table GRANT SELECT ON TABLE ..
TO ``; -- Option 2: Give read access to all tables in the schema GRANT SELECT ON ALL TABLES IN SCHEMA . TO ``; ``` ### Create an output schema{% #create-an-output-schema %} Run the following commands to create a schema where Datadog Experiments can write intermediate results and temporary tables. Replace `datadog_experiments_output` with your output schema name, and `` and `` with the appropriate values. ```sql CREATE SCHEMA IF NOT EXISTS .datadog_experiments_output; GRANT USE SCHEMA ON SCHEMA .datadog_experiments_output TO ``; GRANT CREATE TABLE ON SCHEMA .datadog_experiments_output TO ``; ``` ### Configure a volume for temporary data staging{% #configure-a-volume-for-temporary-data-staging %} Datadog Experiments uses a [volume](https://docs.databricks.com/aws/en/sql/language-manual/sql-ref-volumes) to temporarily save exposure data before copying it into a Databricks table. Run the following commands to create and grant access to this volume. Replace `datadog_experiments_output` with your output schema name, and `` and `` with the appropriate values. ```sql CREATE VOLUME IF NOT EXISTS .datadog_experiments_output.datadog_experiments_volume; GRANT READ VOLUME ON VOLUME .datadog_experiments_output.datadog_experiments_volume TO ``; GRANT WRITE VOLUME ON VOLUME .datadog_experiments_output.datadog_experiments_volume TO ``; ``` ### Grant SQL warehouse access{% #grant-sql-warehouse-access %} Grant the service principal access to the SQL warehouse that Datadog Experiments uses to run queries. 1. Navigate to **SQL Warehouses** in your Databricks Workspace. 1. Select the warehouse for Datadog Experiments. 1. At the top right corner, click **Permissions**. 1. Grant the service principal the **Can use** permission. 1. Close the **Manage permissions** modal. ## Step 2: Connect Databricks to Datadog{% #step-2-connect-databricks-to-datadog %} To connect your Databricks Workspace to Datadog for warehouse-native experiment analysis: 1. Navigate to [Datadog's integrations page](https://app.datadoghq.com/integrations/) and search for **Databricks**. 1. Click the **Databricks** tile to open its modal. 1. Select the **Configure** tab and click **Add Databricks Workspace**. If this is your first Databricks account, the setup form appears automatically. 1. Under the **Connect a new Databricks Workspace** section, enter: - **Workspace Name**. - **Workspace URL**. - **Client ID**. - **Client Secret**. - **System Tables SQL Warehouse ID**. 1. Toggle off **Jobs Monitoring** and all other products. 1. Toggle off the **Metrics - Model Serving** resource. 1. Click **Save Databricks Workspace**. ## Step 3: Configure experiment settings{% #step-3-configure-experiment-settings %} {% alert level="info" %} Datadog supports one warehouse connection per organization. Connecting Databricks replaces any existing warehouse connection (for example, Snowflake). {% /alert %} After you set up your Databricks integration and workspace, configure the experiment settings in Datadog: 1. Open [Datadog Product Analytics](https://app.datadoghq.com/product-analytics). 1. In the left navigation, hover over **Settings** and click **Experiments**. 1. Select the **Warehouse Connections** tab. 1. Click **Connect a data warehouse**. If you already have a warehouse connected, click **Edit** instead. 1. Select the **Databricks** tile. 1. Using the **Account** dropdown, select the Databricks Workspace you configured in Step 2. 1. Enter the **Catalog**, **Schema**, and **Volume name** you configured in Step 1. If your catalog and schema do not appear in the dropdown, enter them manually to add them to the list. 1. Click **Save**. {% image source="https://docs.dd-static.net/images/product_analytics/experiment/guide/databricks_experiment_setup_1.e1533cca70cab80e83987df6faaab3e3.png?auto=format&fit=max&w=850 1x, https://docs.dd-static.net/images/product_analytics/experiment/guide/databricks_experiment_setup_1.e1533cca70cab80e83987df6faaab3e3.png?auto=format&fit=max&w=850&dpr=2 2x" alt="The Edit Data Warehouse modal with Databricks selected, showing input fields for Account, Catalog, Schema, and Volume Name." /%} After you save your warehouse connection, [create experiment metrics](https://docs.datadoghq.com/experiments/defining_metrics.md) using your Databricks data. ## Further reading{% #further-reading %} - [Volumes](https://docs.databricks.com/aws/en/sql/language-manual/sql-ref-volumes) - [How to bridge speed and quality in experiments through unified data](https://www.datadoghq.com/blog/experimental-data-datadog/)