OpenMetadata/openmetadata-docs/content/v1.4.x-SNAPSHOT/connectors/database/databricks/yaml.md

---
title: Run the Databricks Connector Externally
slug: /connectors/database/databricks/yaml
---

{% connectorDetailsHeader
name="Databricks"
stage="PROD"
platform="OpenMetadata"
availableFeatures=["Metadata", "Query Usage", "Lineage", "Column-level Lineage", "Data Profiler", "Data Quality", "dbt", "Tags"]
unavailableFeatures=["Owners", "Stored Procedures"]
/ %}

{% note %}
As per the [documentation](https://docs.databricks.com/en/data-governance/unity-catalog/tags.html#manage-tags-with-sql-commands) here, note that we only support metadata `tag` extraction for databricks version 13.3 version and higher.
{% /note %}

In this section, we provide guides and references to use the Databricks connector.

Configure and schedule Databricks metadata and profiler workflows from the OpenMetadata UI:

- [Requirements](#requirements)
- [Metadata Ingestion](#metadata-ingestion)
- [Query Usage](#query-usage)
- [Lineage](#lineage)
- [Data Profiler](#data-profiler)
- [Data Quality](#data-quality)
- [dbt Integration](#dbt-integration)

{% partial file="/v1.4/connectors/external-ingestion-deployment.md" /%}

## Requirements

### Python Requirements

To run the Databricks ingestion, you will need to install:

```bash
pip3 install "openmetadata-ingestion[databricks]"
```

## Metadata Ingestion

All connectors are defined as JSON Schemas.
[Here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/entity/services/connections/database/databricksConnection.json)
you can find the structure to create a connection to Databricks.

In order to create and run a Metadata Ingestion workflow, we will follow
the steps to create a YAML configuration able to connect to the source,
process the Entities if needed, and reach the OpenMetadata server.

The workflow is modeled around the following
[JSON Schema](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/workflow.json)

### 1. Define the YAML Config

This is a sample config for Databricks:

{% codePreview %}

{% codeInfoContainer %}

#### Source Configuration - Service Connection

{% codeInfo srNumber=1 %}

**catalog**: Catalog of the data source(Example: hive_metastore). This is optional parameter, if you would like to restrict the metadata reading to a single catalog. When left blank, OpenMetadata Ingestion attempts to scan all the catalog.

{% /codeInfo %}

{% codeInfo srNumber=2 %}

**databaseSchema**: DatabaseSchema of the data source. This is optional parameter, if you would like to restrict the metadata reading to a single databaseSchema. When left blank, OpenMetadata Ingestion attempts to scan all the databaseSchema.

{% /codeInfo %}

{% codeInfo srNumber=3 %}

**hostPort**: Enter the fully qualified hostname and port number for your Databricks deployment in the Host and Port field.

{% /codeInfo %}

{% codeInfo srNumber=4 %}

**token**: Generated Token to connect to Databricks.

{% /codeInfo %}

{% codeInfo srNumber=5 %}

**httpPath**: Databricks compute resources URL.

{% /codeInfo %}

{% codeInfo srNumber=6 %}

**connectionTimeout**: The maximum amount of time (in seconds) to wait for a successful connection to the data source. If the connection attempt takes longer than this timeout period, an error will be returned.

{% /codeInfo %}


{% partial file="/v1.4/connectors/yaml/database/source-config-def.md" /%}

{% partial file="/v1.4/connectors/yaml/ingestion-sink-def.md" /%}

{% partial file="/v1.4/connectors/yaml/workflow-config-def.md" /%}

#### Advanced Configuration

{% codeInfo srNumber=7 %}

**Connection Options (Optional)**: Enter the details for any additional connection options that can be sent to Athena during the connection. These details must be added as Key-Value pairs.

{% /codeInfo %}

{% codeInfo srNumber=8 %}

**Connection Arguments (Optional)**: Enter the details for any additional connection arguments such as security or protocol configs that can be sent to Athena during the connection. These details must be added as Key-Value pairs.

- In case you are using Single-Sign-On (SSO) for authentication, add the `authenticator` details in the Connection Arguments as a Key-Value pair as follows: `"authenticator" : "sso_login_url"`

{% /codeInfo %}

{% /codeInfoContainer %}

{% codeBlock fileName="filename.yaml" %}

```yaml
source:
  type: databricks
  serviceName: local_databricks
  serviceConnection:
    config:
      type: Databricks
```
```yaml {% srNumber=1 %}
      catalog: hive_metastore
```
```yaml {% srNumber=2 %}
      databaseSchema: default
```
```yaml {% srNumber=3 %}
      token: <databricks token>
```
```yaml {% srNumber=4 %}
      hostPort: <databricks connection host & port>
```
```yaml {% srNumber=5 %}
      httpPath: <http path of databricks cluster>
```
```yaml {% srNumber=6 %}
      connectionTimeout: 120
```
```yaml {% srNumber=7 %}
      # connectionOptions:
      #   key: value
```
```yaml {% srNumber=8 %}
      # connectionArguments:
      #   key: value
```


{% partial file="/v1.4/connectors/yaml/database/source-config.md" /%}

{% partial file="/v1.4/connectors/yaml/ingestion-sink.md" /%}

{% partial file="/v1.4/connectors/yaml/workflow-config.md" /%}

{% /codeBlock %}

{% /codePreview %}

{% partial file="/v1.4/connectors/yaml/ingestion-cli.md" /%}

{% partial file="/v1.4/connectors/yaml/query-usage.md" variables={connector: "databricks"} /%}

{% partial file="/v1.4/connectors/yaml/lineage.md" variables={connector: "databricks"} /%}

{% partial file="/v1.4/connectors/yaml/data-profiler.md" variables={connector: "databricks"} /%}

{% partial file="/v1.4/connectors/yaml/data-quality.md" /%}

## dbt Integration

You can learn more about how to ingest dbt models' definitions and their lineage [here](/connectors/ingestion/workflows/dbt).