Do I need a separate data catalog if I use Unity Catalog?

Summary

• In most cases, no. Unity Catalog is itself the enterprise data catalog for everything you run on Databricks, covering tables, files, ML models, functions, notebooks, and dashboards under one permission and lineage model.
• A catalog earns its place through discovery, business context, lineage, access control, and audit. Unity Catalog provides all of these natively, so a second catalog usually just duplicates work.
• Unity Catalog is open and interoperable: it implements the Iceberg REST Catalog API and can federate an existing Hive metastore or AWS Glue catalog, so external engines and teams share the same governed metadata.
• The usual reason to still touch another catalog is a large metastore mid-migration or a non-Databricks system standardized elsewhere. Unity Catalog federates rather than replaces those, so you avoid running two governance models in parallel.

Do I need a separate data catalog if I use Unity Catalog?
In most cases, no. Unity Catalog is the enterprise data catalog for everything you run on Databricks, and it governs tables, files, machine learning models, functions, notebooks, and dashboards through one permission model and one lineage graph. Standing up a second catalog next to it usually duplicates the discovery, tagging, and access rules you already have, and forces you to keep two systems in sync. The stronger move is to make Unity Catalog the system of record and let it federate the external catalogs you already run.

What a data catalog is supposed to do

A data catalog is the inventory and control point for an organization's data. At minimum it should let people find and understand data (metadata, search, descriptions, business tags), see where data came from and where it flows (lineage), control who can access what (fine-grained permissions), and prove that control after the fact (audit). A catalog that only stores metadata but can't enforce access, or can't trace lineage into your pipelines, leaves gaps that a second tool then tries to fill. The value is in having one place that does all of it.

What Unity Catalog covers as an enterprise catalog

Unity Catalog treats data and AI assets as securable objects and applies the same governance to all of them.

Capability a catalog must provide	How Unity Catalog delivers it
Metadata and discovery	A centralized metadata layer plus Catalog Explorer for browsing and search, with AI-generated table and column descriptions.
Business semantics	Governed tags for classifying assets, certified and deprecated markers for quality signals, and automatic classification that flags PII and sensitive data.
Lineage	End-to-end lineage down to the column level, tracing how data flows through tables, notebooks, jobs, and pipelines, and from models back to their training data.
Access control	Fine-grained controls including row-level filtering and column-level masking, plus attribute-based access control (ABAC) for policies that apply by tag.
Audit	Detailed logging of data access and actions across the account for security and compliance review.
Scope of assets	Tables and views (Delta Lake and Apache Iceberg), volumes for any file type, functions, registered ML models, features, notebooks, and dashboards.
Open access	REST APIs for programmatic use and an implementation of the Iceberg REST Catalog API for external engines.

Because these live in one product, a permission change, a new tag, or a deletion applies everywhere at once, which is exactly what a separate catalog struggles to guarantee.

How Unity Catalog interoperates instead of forcing a second catalog

Unity Catalog is built to be open, so you rarely need a parallel catalog even in a mixed environment. It implements the Iceberg REST Catalog API, which lets external Iceberg engines read the tables it governs and lets you treat Unity Catalog as the single catalog for an Iceberg estate, even when other tools produce the data. Through federation, an existing Hive metastore or AWS Glue catalog can be connected directly to Unity Catalog so its governance applies on top, without a manual metadata migration. That lets you bring legacy catalogs under one set of controls incrementally rather than maintaining them forever. See the Unity Catalog documentation and the overview of Unity Catalog and Apache Iceberg interoperability.

When you might still keep another catalog

Two situations are common. First, a large existing Hive metastore or Glue estate that you are migrating gradually: federation lets Unity Catalog govern it during the transition instead of a hard cutover. Second, a broad enterprise metadata catalog that spans many non-Databricks systems across the company; even then, Unity Catalog stays the catalog for your Databricks data and AI, and its open APIs let the two share metadata rather than duplicate it. What you want to avoid is running two full governance models over the same Databricks data, since that is where drift and compliance gaps appear.

FAQs

Is Unity Catalog only for tables?

No. It governs tables and views, files in volumes, functions, registered ML models, features, notebooks, and dashboards. Governing data and AI assets in one place is a large part of why a separate catalog is usually unnecessary.

Can Unity Catalog work with catalogs I already have?

Yes. It implements the Iceberg REST Catalog API for external engines and can federate an existing Hive metastore or AWS Glue catalog, applying its governance without requiring you to migrate metadata first.

Does a separate catalog give me anything Unity Catalog lacks?

Rarely, for Databricks data. Discovery, tagging, lineage, fine-grained access control, and audit are all native. A second catalog mostly adds another copy of that metadata to keep current.

What about governing non-Databricks systems?

Unity Catalog can reach beyond Databricks through federation and open APIs. For companies that run a company-wide metadata catalog over many platforms, Unity Catalog interoperates with it while remaining the control point for the Databricks estate.