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Databricks Pipeline

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

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

To run the Ingestion via the UI you'll need to use the OpenMetadata Ingestion Container, which comes shipped with custom Airflow plugins to handle the workflow deployment. If you want to install it manually in an already existing Airflow host, you can follow this guide.

If you don't want to use the OpenMetadata Ingestion container to configure the workflows via the UI, then you can check the following docs to run the Ingestion Framework in any orchestrator externally.

OpenMetadata 0.12 or later

To deploy OpenMetadata, check the Deployment guides.

The first step is to ingest the metadata from your sources. To do that, you first need to create a Service connection first.

This Service will be the bridge between OpenMetadata and your source system.

Once a Service is created, it can be used to configure your ingestion workflows.

Visit Services Page

Select your Service Type and Add a New Service

Click on Add New Service to start the Service creation.

Create a new Service

Add a new Service from the Services page

Select Databricks Pipeline as the Service type and click Next.

Select Service

Select your Service from the list

Provide a name and description for your Service.

OpenMetadata uniquely identifies Services by their Service Name. Provide a name that distinguishes your deployment from other Services, including the other Databricks Pipeline Services that you might be ingesting metadata from.

Note that when the name is set, it cannot be changed.

Add New Service

Provide a Name and description for your Service

In this step, we will configure the connection settings required for Databricks Pipeline.

Please follow the instructions below to properly configure the Service to read from your sources. You will also find helper documentation on the right-hand side panel in the UI.

Configure Service connection

Configure the Service connection by filling the form

  • Host and Port: Enter the fully qualified hostname and port number for your Databricks Pipeline deployment in the Host and Port field.
  • Token: Generated Token to connect to Databricks Pipeline.
  • HTTP Path: Databricks Pipeline compute resources URL.
  • 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"

Once the credentials have been added, click on Test Connection and Save the changes.

Test Connection

Test the connection and save the Service

In this step we will configure the metadata ingestion pipeline, Please follow the instructions below

Configure Metadata Ingestion

Configure Metadata Ingestion Page

  • Name: This field refers to the name of ingestion pipeline, you can customize the name or use the generated name.
  • Pipeline Filter Pattern (Optional): Use to pipeline filter patterns to control whether or not to include pipeline as part of metadata ingestion.
    • Include: Explicitly include pipeline by adding a list of comma-separated regular expressions to the Include field. OpenMetadata will include all pipeline with names matching one or more of the supplied regular expressions. All other schemas will be excluded.
    • Exclude: Explicitly exclude pipeline by adding a list of comma-separated regular expressions to the Exclude field. OpenMetadata will exclude all pipeline with names matching one or more of the supplied regular expressions. All other schemas will be included.
  • Include lineage (toggle): Set the Include lineage toggle to control whether to include lineage between pipelines and data sources as part of metadata ingestion.
  • Enable Debug Log (toggle): Set the Enable Debug Log toggle to set the default log level to debug.
  • Mark Deleted Pipelines (toggle): Set the Mark Deleted Pipelines toggle to flag pipelines as soft-deleted if they are not present anymore in the source system.

Scheduling can be set up at an hourly, daily, weekly, or manual cadence. The timezone is in UTC. Select a Start Date to schedule for ingestion. It is optional to add an End Date.

Review your configuration settings. If they match what you intended, click Deploy to create the service and schedule metadata ingestion.

If something doesn't look right, click the Back button to return to the appropriate step and change the settings as needed.

After configuring the workflow, you can click on Deploy to create the pipeline.

Schedule the Workflow

Schedule the Ingestion Pipeline and Deploy

Once the workflow has been successfully deployed, you can view the Ingestion Pipeline running from the Service Page.

View Ingestion Pipeline

View the Ingestion Pipeline from the Service Page

If there were any errors during the workflow deployment process, the Ingestion Pipeline Entity will still be created, but no workflow will be present in the Ingestion container.

  • You can then Edit the Ingestion Pipeline and Deploy it again.
  • From the Connection tab, you can also Edit the Service if needed.