(databricks_agent)=

# Databricks agent example

```{eval-rst}
.. tags:: Spark, Integration, DistributedComputing, Data, Advanced
```

Flyte can be integrated with the [Databricks](https://www.databricks.com/) service,
enabling you to submit Spark jobs to the Databricks platform.

## Installation

The Databricks agent comes bundled with the Spark plugin. To install the Spark plugin, run the following command:

```
pip install flytekitplugins-spark

```

## Example usage

For a usage example, see {doc}`Databricks agent example usage<databricks_agent_example_usage>`.

## Local testing

To test the Databricks agent locally, create a class for the agent task that inherits from [AsyncAgentExecutorMixin](https://github.com/flyteorg/flytekit/blob/master/flytekit/extend/backend/base_agent.py#L262). This mixin can handle asynchronous tasks and allows flytekit to mimic FlytePropeller's behavior in calling the agent. For more information, see "[Testing agents locally](https://docs.flyte.org/en/latest/flyte_agents/testing_agents_in_a_local_python_environment.html)".

```{note}

In some cases, you will need to store credentials in your local environment when testing locally.

```

## Flyte deployment configuration

```{note}
If you are using a managed deployment of Flyte, you will need to contact your deployment administrator to configure agents in your deployment.
```

To enable the Databricks agent in your Flyte deployment, see the {ref}`Databricks agent setup guide <deployment-agent-setup-databricks>`.


```{toctree}
:maxdepth: -1
:hidden:

databricks_agent_example_usage
```