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Unity Catalog

Unity Catalog

PROD
Feature List
✓ Metadata
✓ Query Usage
✓ Data Profiler
✓ Data Quality
✓ Lineage
✓ Column-level Lineage
✓ dbt
✓ Sample Data
✓ Reverse Metadata (OpenMetadata Only)
✓ Owners
✓ Tags
✓ Auto-Classification
✕ Stored Procedures
In this section, we provide guides and references to use the Unity Catalog connector. Configure and schedule Unity Catalog metadata workflow from the OpenMetadata UI:

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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

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Requirements

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Python Requirements

We have support for Python versions 3.9-3.11
To run the Unity Catalog ingestion, you will need to install: 
pip3 install "openmetadata-ingestion[databricks]"

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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.

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Metadata and Profiling Permissions

These permissions are required on the catalogs, schemas, and tables from which metadata and profiling information will be ingested. 
GRANT USE CATALOG ON CATALOG <catalog_name> TO `<user>`;
GRANT USE SCHEMA ON SCHEMA <schema_name> TO `<user>`;
GRANT SELECT ON TABLE <table_name> TO `<user>`;
Ensure these grants are applied to all relevant tables for metadata ingestion and profiling operations.

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Usage and Lineage

These permissions enable OpenMetadata to extract query history and construct lineage information. 
GRANT SELECT ON SYSTEM.QUERY.HISTORY TO `<user>`;
GRANT USE SCHEMA ON SCHEMA system.query TO `<user>`;
These permissions allow access to Databricks system tables that track query activity, enabling lineage and usage statistics generation.
Adjust <user>, <catalog_name>, <schema_name>, and <table_name> according to your specific deployment and security requirements.

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Metadata Ingestion

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

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1. Define the YAML Config

This is a sample config for Unity Catalog:

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2. Run with the CLI

First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run: 
metadata ingest -c <path-to-yaml>
Note that from connector to connector, this recipe will always be the same. By updating the YAML configuration, you will be able to extract metadata from different sources.

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Query Usage

The Query Usage workflow will be using the query-parser processor. After running a Metadata Ingestion workflow, we can run Query Usage workflow. While the serviceName will be the same to that was used in Metadata Ingestion, so the ingestion bot can get the serviceConnection details from the server.

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1. Define the YAML Config

This is a sample config for Usage:

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2. Run with the CLI

After saving the YAML config, we will run the command the same way we did for the metadata ingestion: 
metadata usage -c <path-to-yaml>

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Lineage

After running a Metadata Ingestion workflow, we can run Lineage workflow. While the serviceName will be the same to that was used in Metadata Ingestion, so the ingestion bot can get the serviceConnection details from the server.

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1. Define the YAML Config

This is a sample config for Lineage:
  • You can learn more about how to configure and run the Lineage Workflow to extract Lineage data from here

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2. Run with the CLI

After saving the YAML config, we will run the command the same way we did for the metadata ingestion: 
metadata ingest -c <path-to-yaml>

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dbt Integration

You can learn more about how to ingest dbt models’ definitions and their lineage here.