docs/databricks.md
Rama main · réplica de lectura del repositorio original.
---
title: Databricks
noIndex: false
noContent: false
coverImage: fA7kQuiRC6ruDJvzJNwq
---
<Callout status="info">
Beta version available on the Enterprise plan - please talk to our support to get it enabled
</Callout>
With the Databricks integration, you can leverage the capabilities of Deepnote's SQL blocks to query your warehouses at lightning-fast speed.
Deepnote's Databricks integration allows data teams to efficiently query their data, extract relevant data, and start analyzing and modeling in the comfort of their known notebook environment.
### How to connect to Databricks
To create the integration, you'll need a few things. Follow [Databricks guide](https://docs.databricks.com/integrations/jdbc-odbc-bi.html#get-connection-details-for-a-sql-warehouse) to get the connection details for a SQL warehouse.
- **Host**: Databricks server hostname. The hostname of the server you are trying to connect to.
- **Port**: The port on the server of interest you are trying to connect to. The default is port 433.
- **HTTP Path**: Databricks compute resources URL.
- **Token**: Your personal access token. For instructions about how to generate a token, see [Databricks docs](https://docs.databricks.com/dev-tools/auth.html#pat).
- **Schema**: Default schema used by integration. This is equivalent to running `USE <schema_name>`. If you provide no schema the default is `default`.
- **Catalog**: Default catalog used by integration. If you provide no name the default is `hive_metastore`.
### How to use
Once created, you'll be able to connect the Databricks integration to any project within your workspace through the right-hand sidebar. The Databricks integration comes with custom Databricks SQL blocks that help streamline your analytics efforts. You can also convert any existing SQL block to a Databricks block.
As with all SQL blocks, the query results will be saved as a Pandas DataFrame and stored in the variable specified in the SQL block.
In order to enable the SQL blocks to work with Databricks integration, you have to install `sqlalchemy-databricks` in your project. Run the following command in a block to get it installed:
```
!pip install sqlalchemy-databricks
```
Or, alternatively, add it to local Dockerfile and it will get automatically installed and cached for faster project starts.
### Next steps
Jump right into Deepnote & [learn more about SQL blocks in this A/B testing template](https://deepnote.com/launch?template=A/B%20Testing). You can also save yourself some setup work by hitting the `Duplicate` button in the top-right corner to start exploring on your own!
### Secure connections
Deepnote supports securing connections to Databricks via optional [SSH tunnels](/docs/secure-connections).