· Data Platforms  · 2 min read

Governance at Scale: A Guide to Databricks Unity Catalog

The chaos of managing permissions in Databricks is over. Unity Catalog provides a centralised governance layer for all your data and AI assets.

The chaos of managing permissions in Databricks is over. Unity Catalog provides a centralised governance layer for all your data and AI assets.

For years, Databricks permissions were a mess. You had to manage them separately for each Workspace. If you had a “Prod” workspace and a “Dev” workspace, you had to define who could see the “Sales” table twice.

Unity Catalog fixes this. It is a unified governance layer that sits above your workspaces.

One Metastore to Rule them All

With Unity Catalog, you define your users and data permissions once, at the Account level.

  • GRANT SELECT ON TABLE sales TO group finance; This applies everywhere. It doesn’t matter which workspace the Finance team logs into; the rule holds.

Capabilities

  1. Data Lineage: It automatically tracks how data moves. You can see a column in a dashboard and trace it all the way back to the raw S3 file it came from. This is magic for debugging.
  2. Fine-Grained Access: You can filter rows and masks columns.
    • “Analysts can see the customers table, but mask the email column.”
    • “German employees can only see rows where country = 'DE'.”
  3. Audit Logs: You get a complete log of who accessed what data and when.

The Migration

Moving to Unity Catalog usually requires upgrading your Hive Metastore. It’s a non-trivial migration, but essential for security.

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