OpenMetadata/openmetadata-docs/content/v1.7.x/connectors/pipeline/databricks-pipeline/yaml.md

title	slug
Run the Databricks Pipeline Connector Externally	/connectors/pipeline/databricks-pipeline/yaml

{% connectorDetailsHeader name="Databricks" stage="PROD" platform="OpenMetadata" availableFeatures=["Pipelines", "Pipeline Status", "Usage"] unavailableFeatures=["Owners", "Tags", "Lineage"] / %}

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

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

• Requirements
• Metadata Ingestion

{% partial file="/v1.7/connectors/external-ingestion-deployment.md" /%}

Requirements

Python Requirements

{% partial file="/v1.7/connectors/python-requirements.md" /%}

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

pip3 install "openmetadata-ingestion[databricks]"

Metadata Ingestion

All connectors are defined as JSON Schemas. Here you can find the structure to create a connection to Databricks Pipeline.

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

1. Define the YAML Config

This is a sample config for Databricks Pipeline:

{% codePreview %}

{% codeInfoContainer %}

Source Configuration - Service Connection

{% codeInfo srNumber=1 %}

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

{% /codeInfo %}

{% codeInfo srNumber=2 %}

Token: Generated Token to connect to Databricks Pipeline.

{% /codeInfo %}

{% codeInfo srNumber=3 %}

Connection Arguments (Optional): Enter the details for any additional connection arguments such as security or protocol configs that can be sent to Databricks 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"

HTTP Path: Databricks Pipeline compute resources URL.

{% /codeInfo %}

{% partial file="/v1.7/connectors/yaml/pipeline/source-config-def.md" /%}

{% partial file="/v1.7/connectors/yaml/ingestion-sink-def.md" /%}

{% partial file="/v1.7/connectors/yaml/workflow-config-def.md" /%}

{% /codeInfoContainer %}

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

source:
  type: databrickspipeline
  serviceName: local_databricks_pipeline
  serviceConnection:
    config:
      type: DatabricksPipeline

      hostPort: localhost:443

      token: <databricks token>

      connectionArguments:
        http_path: <http path of databricks cluster>

{% partial file="/v1.7/connectors/yaml/pipeline/source-config.md" /%}

{% partial file="/v1.7/connectors/yaml/ingestion-sink.md" /%}

{% partial file="/v1.7/connectors/yaml/workflow-config.md" /%}

{% /codeBlock %}

{% /codePreview %}

{% partial file="/v1.7/connectors/yaml/ingestion-cli.md" /%}