Databricks Pipeline | Collate Integration Guide

Configure metadata ingestion from Databricks pipelines for job flows, dependencies, and structured datasets.

In this section, we provide guides and references to use the Databricks Pipeline connector. Configure and schedule Databricks Pipeline metadata workflows from the Collate UI:

• Metadata Ingestion
• Troubleshooting

Metadata Ingestion

Connection Details

1. Connection Details

   When using a Hybrid Ingestion Runner, any sensitive credential fields—such as passwords, API keys, or private keys—must reference secrets using the following format:

   password: secret:/my/database/password
   

   This applies only to fields marked as secrets in the connection form (these typically mask input and show a visibility toggle icon). For a complete guide on managing secrets in hybrid setups, see the Hybrid Ingestion Runner Secret Management Guide.

   • 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"
2. Test the Connection

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

   
3. Configure Metadata Ingestion

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

   

   Metadata Ingestion Options

   • 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.
4. Schedule the Ingestion and Deploy

   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.

   
5. View the Ingestion Pipeline

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

   

   If AutoPilot is enabled, workflows like usage tracking, data lineage, and similar tasks will be handled automatically. Users don’t need to set up or manage them - AutoPilot takes care of everything in the system.