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Tip: As per the documentation, Collate only supports metadata tag extraction for Databricks version 13.3 and higher.
This section provides guides and references to use the Databricks connector. Configure and schedule Databricks metadata and profiler workflows from the Collate UI:
Note: If Databricks exposes an Iceberg-backed table through its standard catalog and table metadata, use the same Databricks connector workflow described on this page. This page does not document separate Iceberg-specific configuration or guarantees for the Databricks connector.

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 ingesting metadata from Databricks, grant the following permissions to the user account Collate will use to connect.

Permission Requirement

To enable full functionality of metadata extraction, profiling, usage, and lineage features in Collate, 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 Collate to extract query history and construct lineage information.
These permissions allow access to Databricks system tables that track query activity, enabling lineage and usage statistics generation.
Tip: Adjust <user>, <catalog_name>, <schema_name>, and <table_name> according to your specific deployment and security requirements.

Unity Catalog

If you use Unity Catalog in Databricks, see the Unity Catalog connector.

Metadata Ingestion

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

Step 1: Add New Service

  1. In the left navigation, click Connections.
  2. On the Connections page, 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. Collate 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.
Add New Service Name
Note: The service name cannot be changed after it is set.

Step 4: Configure Connection Options

Specify where ingestion runs, provide your source credentials, and verify the connection.

Select Ingestion Runner

Select an Ingestion Runner: the runner where the ingestion pipeline will execute. Add Name and Select Ingestion Runner

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
Tip: 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:
This applies only to fields marked as secrets in the connection form (these typically mask input and show a visibility toggle icon). For more information about 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 deployment in the Host and Port field.
  • Token: Generated Token to connect to Databricks.
  • HTTP Path: Databricks compute resources URL.
  • 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, Collate returns an error.
  • Catalog: Catalog of the data source (for example, hive_metastore). This optional parameter restricts metadata reading to a single catalog. When left blank, Collate ingestion scans all catalogs.
  • DatabaseSchema: The database schema of the data source. This optional parameter restricts metadata reading to a single schema. When left blank, Collate ingestion scans all schemas.

Advanced Configuration

Database Services have an Advanced Configuration section, where you can pass extra arguments to the connector and, if needed, change the connection scheme. This is required only for advanced connectivity scenarios or customizations.
  • Connection Options (Optional): Enter the details for any additional connection options that can be sent to the database during the connection. These details must be added as key-value pairs.
  • Connection Arguments (Optional): Enter the details for any additional connection arguments such as security or protocol configs that can be sent during the connection. These details must be added as key-value pairs.

Test Connection

After adding the credentials, 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 Collate 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. Collate 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. Collate 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.
Tip: Leave all filter patterns empty to ingest all databases, schemas, and tables available in the source.
Filter Options The Database, Schema, Table, and Stored Procedure sections each include the following filter options:
  • Database: Controls which databases Collate 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.
  • Stored Procedure: Controls which stored procedures are included in metadata ingestion.
Each section provides the following controls:
  • Scan Mode: 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.
Tip: If AutoPilot is enabled, usage tracking, data lineage, and other downstream workflows start automatically after the first metadata ingestion completes.

Step 6: Create & Deploy

Click Create & Deploy to deploy the agent and start the first metadata ingestion run. Collate 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 Connections in the left navigation 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 Collate catalog in sync. It powers discovery, lineage, and governance across your data assets. When you click Create & Deploy, Collate 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. In the left navigation, click Connections and select your service.
  2. Click the Agents tab.
  3. Click Add Agent and select Metadata from the dropdown. 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 5: 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:
      Note: 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.

Reverse Metadata

  • Description Management: Databricks supports description updates at all levels:
    • Database level
    • Schema level
    • Table level
    • Column level
  • Owner Management: Databricks supports owner management at the following levels:
    • Database level
    • Schema level
    • Table level
    Tip: Databricks does not support to set null as owner.
    Note: Databricks identifies user principals by email, not by username. When reverse ingestion updates a user owner, Collate uses the assigned user’s email address when available and falls back to the Collate username otherwise. Collate resolves teams by team name—the team must match a Databricks account-level group.
  • Tag Management: Databricks supports tag management at all levels:
    • Database level
    • Schema level
    • Table level
    • Column level
  • Custom SQL Template: Databricks supports custom SQL templates for metadata changes. The template is interpreted using python f-strings. Here are examples of custom SQL queries for metadata changes:
    The list of variables for custom SQL can be found here.
  • Requirements for Reverse Metadata: In addition to the basic ingestion requirements, for reverse metadata ingestion the user needs:
For more information about reverse metadata ingestion, see Reverse Metadata Application.

Troubleshooting

Databricks Troubleshooting

Learn more about how to troubleshoot common Databricks connector issues and resolve configuration or ingestion errors.