This guide shows how to manage data and data access in Databricks. For information on Databricks security, see the Security and compliance guide. Databricks provides centralized governance for data and AI with Unity Catalog and Delta Sharing.
Unity Catalog is a fine-grained governance solution for data and AI on the Databricks platform. It helps simplify security and governance of your data and AI assets by providing a central place to administer and audit access to data and AI assets.
For best practices on adopting Unity Catalog, see Unity Catalog best practices.
Databricks Catalog Explorer provides a UI to explore and manage data and AI assets, including schemas (databases), tables, volumes (non-tabular data), and registered ML models, along with asset permissions, data owners, external locations, and credentials. You can use the Insights tab in Catalog Explorer to view the most frequent recent queries and users of any table registered in Unity Catalog.
Databricks provides access to audit logs of activities performed by Databricks users, allowing your enterprise to monitor detailed Databricks usage patterns.
Every good data governance story starts with a strong identity foundation. To learn how to best configure identity in Databricks, see Identity best practices.
Table access control is a legacy data governance model that lets you programmatically grant and revoke access to objects managed by your workspace’s built-in Hive metastore. Databricks recommends that you use Unity Catalog instead of table access control. Unity Catalog simplifies security and governance of your data by providing a central place to administer and audit data access across multiple workspaces in your account.