---
title: Databricks
description: How to connect to Databricks.
section_name: Data
maturity: public-preview

---

# Databricks {: #databricks }

Connecting to Databricks is currently certified through Azure and AWS.


!!! info "Public preview"
    Support for Databricks is on by default.

    Feature flag(s):
    
    - **Enable Databricks Driver**: Allows you to connect to and add snapshotted data from Databricks.
    - **Enable Databricks Wrangling**: Allows you to perform data wrangling on Databricks datasets in Workbench.
    - **Enable Databricks In-Source Materialization in Workbench**: Allows you to materialize wrangled datasets in Databricks as well as the Data Registry.
    - **Enable Dynamic Datasets in Workbench**: Allows you to add dynamic Databricks data to a Use Case&mdash;enabling the ability to view live samples, perform data wrangling, and initiate in-source materialization.

## Supported authentication {: #supported-authentication }

- Personal access token

## Prerequisites {: #prerequisites }

The following is required before connecting to Databricks in DataRobot:

=== "Azure"
    - A [Databricks workspace](https://learn.microsoft.com/en-us/azure/databricks/getting-started/#--create-an-azure-databricks-workspace){ target=_blank } in the Azure Portal app
    - Data stored in an Azure Databricks database

=== "AWS"
    - A [Databricks workspace](https://docs.databricks.com/en/administration-guide/workspace/index.html){ target=_blank } in AWS
    - Data stored in an AWS Databricks database


## Generate a personal access token {: #generate-a-personal-access-token }

=== "Azure"
    In the Azure Portal app, generate a personal access token for your Databricks workspace. This token will be used to authenticate your connection to Databricks in DataRobot. 

    See the [Azure Databricks documentation](https://learn.microsoft.com/en-us/azure/databricks/dev-tools/auth#--azure-databricks-personal-access-tokens-for-workspace-users){ target=_blank }.

=== "AWS"
    In AWS, generate a personal access token for your Databricks workspace. This token will be used to authenticate your connection to Databricks in DataRobot. 

    See the [Databricks on AWS documentation](https://docs.databricks.com/en/dev-tools/auth.html#databricks-personal-access-token-authentication){ target=_blank }.

## Set up a connection in DataRobot {: #set-up-a-connection-in-datarobot }

To connect to Databricks in DataRobot (note that this example uses Azure):

1. Open **Workbench** and select a Use Case.
2. Follow the instructions for [connecting to a data source](wb-connect#connect-to-a-data-source){ target=_blank }. 
3. With the information retrieved in the [previous section](#generate-a-personal-access-token), fill in the [required configuration parameters](#required-parameters).

    ![](images/wb-databricks-2.png)

4. Under **Authentication**, click **New credentials**. Then, enter your access token and a unique display name. If you've previously added credentials for this data source, you can select it from your saved credentials.

    ![](images/wb-databricks-3.png)

5. Click **Save**.

## Required parameters {: #required-parameters }

The table below lists the minimum required fields to establish a connection with Databricks:

=== "Azure"
    Required field | Description |  Documentation
    --------------- | ---------- |  -----------
    Server Hostname       | The address of the server to connect to.  |    [Azure Databricks documentation](https://learn.microsoft.com/en-us/azure/databricks/integrations/jdbc-odbc-bi#--retrieve-the-connection-details){ target=_blank }
    HTTP Path       | The compute resources URL.  |    [Azure Databricks documentation](https://learn.microsoft.com/en-us/azure/databricks/integrations/jdbc-odbc-bi#--retrieve-the-connection-details){ target=_blank }

=== "AWS"
    Required field | Description |  Documentation
    --------------- | ---------- |  -----------
    Server Hostname       | The address of the server to connect to.  |    [Databricks on AWS documentation](https://docs.databricks.com/en/integrations/jdbc-odbc-bi.html#retrieve-the-connection-details){ target=_blank }
    HTTP Path       | The compute resources URL.  |    [Databricks on AWS documentation](https://docs.databricks.com/en/integrations/jdbc-odbc-bi.html#retrieve-the-connection-details){ target=_blank }

SQL warehouses are dedicated to execute SQL, and as a result, have less overhead than clusters and often provide better performance. It is recommended to use a SQL warehouse if possible. 

!!! note
    If the `catalog` parameter is specified in a connection configuration, Workbench will only show a list of schemas in that catalog. If this parameter is not specified, Workbench lists all catalogs you have access to.

{% include 'includes/data-conn-trouble.md' %}
