> For the complete documentation index, see [llms.txt](https://help.felt.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.felt.com/upload-anything/data-sources/databricks.md). # Databricks Connect a Databricks SQL warehouse or cluster to Felt. Authenticate with a personal access token or M2M OAuth to map your data. {% hint style="success" %} Available on the Business and Enterprise plans. [Compare plans](https://felt.com/pricing#compare) {% endhint %} Connect a [Databricks](https://www.databricks.com/) SQL warehouse or cluster to add tables and views from your catalogs to maps or query them with [SQL](/upload-anything/sql-queries.md). {% embed url="" %} ## Connect Databricks Set up [credentials](#credentials) for Felt. Then follow the steps in [Add a source](/upload-anything/data-sources/add-a-source.md#connect-a-source) and select **Databricks**. If your Databricks workspace restricts inbound connections by IP address, [allow Felt's IP addresses](/upload-anything/data-sources/add-a-source.md#allow-felts-ip-addresses). In **Connection**, fill in these fields, or click **Autofill** and paste a connection string such as `databricks://token:dapi123abc@mycompany.cloud.databricks.com:443/default?http_path=/sql/1.0/warehouses/abc123`. Autofill also copies the token after `token:` into **Personal access token** in **Credentials**. In Databricks, the host and HTTP path are in your SQL warehouse's **Connection details**, or in a cluster's **Advanced options** > **JDBC/ODBC**. | Field | Required | What to enter | | --------- | -------- | ------------------------------------------------------------------------------------------ | | Host | Yes | The server host name, such as `mycompany.cloud.databricks.com`. | | HTTP path | Yes | The HTTP path of the SQL warehouse or cluster, such as `/sql/1.0/warehouses/abc123def456`. | | Catalog | No | One catalog to read. | | Schema | No | One schema to read. Leave it empty to read all schemas. | ## Credentials A Databricks source authenticates with a personal access token or with M2M OAuth. In **Credentials**, select the method you want as the **Authentication type**. Every Felt query uses the source's credentials, so Databricks can't apply row-level security per Felt user. To control who sees which data, connect separate sources with credentials limited to the appropriate tables or views, then share each source with the right people. See [Sharing and permissions](/upload-anything/data-sources.md#sharing-and-permissions). ### Personal access token A Databricks [personal access token](https://docs.databricks.com/aws/en/dev-tools/auth/pat) starts with `dapi`. Generate one in Databricks and paste it into **Personal access token**. ### M2M OAuth Machine-to-machine (M2M) OAuth uses a Databricks [service principal](https://docs.databricks.com/aws/en/dev-tools/auth/oauth-m2m) instead of a person's token. 1. In Databricks, create a service principal for Felt and copy its client ID and client secret. 2. In the Databricks workspace you want Felt to read, open the **Catalog** tab and select the catalog. 3. In the **Permissions** tab, click **Grant** and select the service principal. 4. Grant the `SELECT`, `BROWSE`, `USE_CATALOG`, and `USE_SCHEMA` privileges. 5. In Felt, select **M2M OAuth** and enter the **Client ID** and **Client secret**. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://help.felt.com/upload-anything/data-sources/databricks.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.