Data Governance. Databricks Unity Catalog

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Cloud-native · Platform governance (lakehouse)

Databricks Unity Catalog

Unified governance for data and AI in Databricks

Verified: 2026-07-01

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Best for

  • data and AI platform on Databricks
  • lakehouse, access control for tables and models
  • automatic lineage in Databricks

Strengths

  • Native Databricks integration
  • Central access control, audit, and lineage
  • Governance for data and AI assets

Weaknesses

  • Greatest value inside the Databricks ecosystem
  • Does not replace a company-wide catalog for all systems
  • Data outside Databricks needs extra integrations

When to choose

  • Databricks is your central data platform
  • You build lakehouse and AI solutions on Databricks
  • You need column-level lineage and access control

When to avoid

  • You do not use Databricks as the core platform
  • You need company-wide business glossary without lakehouse
  • You want a BI-only catalog without data engineering

Pricing (indicative)

Part of Databricks - cost depends on workspace and usage.

Features

  • Yes - Data catalog
  • No - Business glossary
  • Yes - Lineage
  • No - Data quality
  • Yes - Data classification
  • Yes - Access governance
  • No - Workflow / stewardship
  • Yes - AI context layer

Integrations

Databricks, Delta Lake, MLflow, Spark

Sources and methodology

Comparison ← DG platform catalog

Informational content only. Verify pricing and feature scope with vendors before purchase.