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As per the documentation here, note that we only support metadata tag extraction for Databricks version 13.3 version and higher.
In this section, we provide guides and references to use the Databricks connector.
Supported Authentication Types:
  • Personal Access Token: Token-based workspace authentication generated from User Settings in Databricks
  • Databricks OAuth: OAuth2 Machine-to-Machine authentication using Service Principal credentials
  • Azure AD Setup: Azure Active Directory authentication using Azure Service Principal (for Azure Databricks workspaces)
Configure and schedule Databricks metadata and profiler workflows from the OpenMetadata UI:

How to Run the Connector Externally

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, instead, you want to manage your workflows externally on your preferred orchestrator, you can check the following docs to run the Ingestion Framework anywhere.

External Schedulers

Get more information about running the Ingestion Framework Externally

Requirements

Before configuring the Databricks connector, make sure the following requirements are in place.

Permission Requirement

To enable full functionality of metadata extraction, profiling, usage, and lineage features in OpenMetadata, the following permissions must be granted to the relevant users in your Databricks environment.

Metadata and Profiling Permissions

These permissions are required on the catalogs, schemas, and tables from which metadata and profiling information will be ingested.
Ensure these grants are applied to all relevant tables for metadata ingestion and profiling operations.

Usage and Lineage

These permissions enable OpenMetadata to extract query history and construct lineage information.
These permissions allow access to Databricks system tables that track query activity and lineage relationships, enabling lineage and usage statistics generation.

View Definitions

To extract view definitions, the user needs access to the information schema:

Tags (Databricks 13.3+)

To extract Databricks tags on catalogs, schemas, tables, and columns, the following permissions are required:
Tag extraction requires Databricks Runtime 13.3 or higher. If your cluster is running an older version, tags will not be extracted.
Adjust <user>, <catalog_name>, <schema_name>, and <table_name> according to your specific deployment and security requirements.

Unity Catalog

If you are using Unity Catalog in Databricks, then checkout the Unity Catalog connector.

Metadata Ingestion

To ingest metadata from Databricks, you need to create a service connection. The service connects Databricks with OpenMetadata. Once you create a service, OpenMetadata automatically starts ingesting metadata.

Step 1: Add New Service

  1. Navigate to Settings > Services. Navigate to Settings and Services
  2. Click Add New Service. Add New Service

Step 2: Select a Service and Connector

From the service type dropdown, select Database Services, then click the Databricks connector tile. Select Service

Step 3: Add Service Name and Description

  • Enter a unique, descriptive Service Name. OpenMetadata identifies services by their service name. Enter a name that distinguishes this deployment from other Databricks services you are ingesting metadata from.
  • Optional: Enter a Description for the service.
Configure Service
Note: The service name cannot be changed after it is set.

Step 4: Configure Connection Options

Specify your source credentials and verify the connection.

Enter Connection Details

Enter the connection details for Databricks. The right-hand panel in the UI displays inline help for each field. Configure Service Connection
  • Host and Port: Enter the fully qualified hostname and port number for your Databricks deployment in the Host and Port field.
  • Authentication Type: Choose one of the following authentication methods:
    • Personal Access Token: Provide a token generated from User Settings > Developer > Access Tokens in your Databricks workspace.
    • Databricks OAuth: Provide a clientId and clientSecret for a Service Principal created in your Databricks Account Console.
    • Azure AD Setup: Provide azureClientId, azureClientSecret, and azureTenantId for an Azure Service Principal registered in Azure Active Directory (for Azure Databricks workspaces only).
  • HTTP Path: Databricks compute resources URL.
  • Connection Timeout: 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.
  • Catalog: Catalog of the data source (Example: hive_metastore). This is an 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 catalogs.
  • DatabaseSchema: Database schema of the data source. This is an optional parameter, if you would like to restrict the metadata reading to a single database schema. When left blank, OpenMetadata Ingestion attempts to scan all the database schemas.
  • Query History Table: Table name to fetch the query history from. Defaults to system.query.history.

Test Connection

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

Step 5: Configure Ingestion Options

In the What to Ingest step, use filter patterns to control which assets OpenMetadata ingests from your database service. Filter patterns use regular expressions applied to asset names.

How Filter Patterns Work

  • Include: Add one or more comma-separated regular expressions. OpenMetadata ingests only assets whose names match at least one expression. Leave blank to include all assets.
  • Exclude: Add one or more comma-separated regular expressions. OpenMetadata skips any asset whose name matches an expression. Leave blank to exclude nothing.
Rules match asset names using one of five expressions:
  • contains: matches any name containing the value. For example, sales matches my_sales_data and sales_2024.
  • starts with: matches names beginning with the value. For example, prod_ matches prod_db and prod_schema.
  • ends with: matches names ending with the value. For example, _raw matches events_raw and logs_raw.
  • is exactly: matches the exact name only. For example, analytics matches only analytics.
  • matches regex: matches names using a regular expression. For example, ^prod_.*_v\d+$ matches prod_events_v1.
When both Include and Exclude are set, Exclude takes priority.
Leave all filter patterns empty to ingest all databases, schemas, and tables available in the source.
Filter Options The Database, Schema, and Table sections each include the following filter options:
  • Database: Controls which databases OpenMetadata ingests from the source.
  • Schema: Controls which schemas within the ingested databases are included.
  • Table: Controls which tables and views within the ingested schemas are included.
Each section provides the following controls:
  • Scan Mode: You can choose between the following scan modes:
    • Scan all: Ingests every asset of that type the connector can access. This is the default.
    • Only specific: Enables include rules so only assets matching at least one rule are ingested.
  • Exclude system toggle: Use this toggle to automatically filter out system-reserved names defined by the connector, for example, Exclude system databases for the Databases section.
  • Always exclude: Add permanent exclusion rules (shown in red). Assets matching these rules are never ingested, regardless of include rules.
  • Preview: Shows a real-time summary of what will be in scope based on your current rules.
  • Include rules (available only in Only specific mode): Click + Add to define a rule. Added rules appear as chips; an asset is included if it matches any rule.

Step 6: Create & Deploy

Click Create & Deploy to deploy the agent and start the first metadata ingestion run. OpenMetadata saves the service configuration and immediately begins pulling metadata from the source. To monitor ingestion progress or view the service you just added, go to Settings > Services and select your service.

Configure Metadata Agent and Schedule Ingestion

The Metadata Agent extracts schemas, tables, columns, and other structural metadata from your source and keeps your OpenMetadata catalog in sync. It powers discovery, lineage, and governance across your data assets. When you click Create & Deploy, OpenMetadata automatically deploys a Metadata Agent for this service and triggers the first ingestion run. View its status and run history from the Agents tab on the service detail page. To configure the additional Metadata Agent and schedule ingestion, follow these steps:
  1. Navigate to Settings > Services and select the service type. Navigate to Settings and Services
  2. Click the service you have added.
  3. Select the Agents tab and click Add Agent > Metadata. Add Metadata Agent For some services, the dropdown is not available and clicking Add Agent takes you directly to the agent configuration page.
  4. On the Configure Ingestion page, do the following and click Next.
    • Name this Ingestion: Enter a unique recognizable name for this ingestion pipeline. Name this Ingestion
    • Agent Setup: Configure core parameters for metadata extraction. The following fields are available: Agent Setup
    • Filter Patterns: Apply include or exclude rules to scope which databases, schemas, tables, and stored procedures this agent ingests. For more information about various filter options, see Step 6: Configure Ingestion Options. Filter Patterns
    • Scope & Behaviour: Control how the agent handles metadata during ingestion. Toggle each option on or off based on your needs:
      Available toggles vary by connector. Stored procedure options only appear for connectors that support stored procedures.
      Scope & Behaviour
    • Advanced Config: Optional connector-specific settings such as Include Views and Extract JSON Schema. Advanced Config
  5. On the Schedule Interval page, set when the agent runs:
    • Schedule: Choose a preset interval (Hourly, Daily, Weekly, Monthly) or enter a custom cron expression.
    • On-Demand: No automatic schedule; trigger the agent manually when needed.
    Schedule Interval
  6. Click Add to deploy the agent.

Usage Workflow

Learn more about how to configure the Usage Workflow to ingest Query information from the UI.

Lineage Workflow

Learn more about how to configure the Lineage from the UI.

Profiler Workflow

Learn more about how to configure the Data Profiler from the UI.

Data Quality Workflow

Learn more about how to configure the Data Quality tests from the UI.

dbt Integration

Learn more about how to ingest dbt models’ definitions and their lineage.