Databricks Unity Catalog vs Snowflake Horizon: governance and catalog

Summary

• Databricks Unity Catalog and Snowflake Horizon are both unified governance-and-catalog layers: each centralizes discovery, access control, lineage, and auditing for an organization's data, and each is extending into AI assets. This is a factual, source-backed side-by-side; verify current capabilities on each vendor's documentation, since both evolve quickly.
• Catalog scope: Unity Catalog governs data (tables, views, volumes, files) plus ML models, functions, AI agents, features, dashboards, and metrics as securable objects. Snowflake Horizon, per Snowflake's documentation, governs Snowflake-managed objects (tables, views, dynamic and materialized tables, ML models, and more) and can surface external data through catalog-linked databases.
• Open formats: Unity Catalog natively supports Delta Lake and Apache Iceberg, and Delta UniForm exposes a single copy of data for reading by Iceberg-compatible engines. Snowflake, per its documentation, supports Apache Iceberg tables and the Iceberg REST Catalog API via Snowflake Open Catalog.
• Access control and lineage: Unity Catalog provides RBAC, attribute-based access control (ABAC), and row and column controls enforced at query time, with automatic column-level lineage. Snowflake Horizon, per its documentation, provides role-based access control, masking and row-access policies, and column-level lineage for Snowflake data.
• AI/agent governance: Unity Catalog governs ML models, functions, and AI agents as first-class securable objects and extends runtime governance to models, tools, and MCP servers through the Unity Gateway.

Databricks Unity Catalog vs Snowflake Horizon: governance and catalog

Databricks Unity Catalog and Snowflake Horizon are both unified governance and catalog layers: each provides a central place to discover, secure, and audit an organization's data, and each is extending into AI and machine-learning assets. The sections below compare them factually across catalog scope, open table formats, lineage, fine-grained access control, multi-engine openness, and AI and agent governance. Capabilities on both platforms change frequently, so verify the current state against each vendor's official documentation before making a decision. Databricks claims below are grounded in Databricks documentation; Snowflake Horizon capabilities are stated as documented by Snowflake.

Catalog scope and breadth

• Unity Catalog governs a wide range of assets as securable objects in one model: tables, views, materialized views, volumes and files, ML models, user-defined functions, features, notebooks, dashboards, business metrics, and AI agents. The same permissions, tags, lineage, and auditing apply across analytics and AI. See the Unity Catalog documentation.
• Snowflake Horizon, per Snowflake's documentation, is the built-in governance and discovery layer for Snowflake. It covers Snowflake-managed objects such as tables, views, dynamic and materialized tables, and ML models, and can surface externally managed data through catalog-linked databases and Iceberg REST APIs.

Open table formats (Delta Lake and Apache Iceberg)

• Unity Catalog natively supports both Delta Lake and Apache Iceberg. With Delta UniForm and Iceberg interoperability, a single copy of data can be read by Iceberg-compatible engines without copy pipelines, and Unity Catalog can federate to other Iceberg catalogs. Databricks also open-sourced Unity Catalog.
• Snowflake, per its documentation, supports Apache Iceberg tables (including Snowflake-managed Iceberg) and exposes the Iceberg REST Catalog API through Snowflake Open Catalog, its managed distribution of Apache Polaris, so external engines can read Iceberg data.

Data lineage

• Unity Catalog automatically captures column-level lineage across tables, views, notebooks, jobs, pipelines, dashboards, and ML models, with no manual instrumentation.
• Snowflake Horizon, per Snowflake's documentation, provides lineage — including column-level lineage — for Snowflake data, along with access-history views for access provenance and auditing.

Fine-grained access control

• Unity Catalog enforces access with hierarchical role-based access control (ANSI SQL GRANT and REVOKE), attribute-based access control (ABAC) using governed tags, and row filters and column masks evaluated at query time. These row and column controls can be enforced for external Iceberg engines through the Iceberg REST Catalog scan-planning APIs.
• Snowflake Horizon, per Snowflake's documentation, provides role-based access control, dynamic data masking, row-access policies, object tagging, and credential vending for governed access from external engines.

Multi-engine and open access

• Unity Catalog exposes open APIs, including the Iceberg REST Catalog for read and write access from external engines such as Apache Spark and other Iceberg-compatible engines, so teams can apply one governance model across engines. Unity Catalog is available as open source.
• Snowflake, per its documentation, provides the Iceberg REST Catalog API through Snowflake Open Catalog (Apache Polaris) for interoperability with external Iceberg engines.

AI and agent governance

• Unity Catalog governs ML models, functions, and AI agents as first-class securable objects, applying the same permissions, tags, lineage, and auditing used for tables. Through the Unity Gateway and runtime governance, Databricks extends governance to models, tools, and MCP servers, and Unity Catalog Glossary and Domains provide governed business semantics for both people and agents. See what's new in Unity Catalog.
• Snowflake, per its documentation, governs its data and AI features within Horizon for Snowflake-managed objects.

Getting started

• Review the Unity Catalog documentation and the Data + AI Summit 2026 Unity Catalog updates.
• Explore open-format interoperability with Apache Iceberg and the open-source Unity Catalog announcement.
• For any Snowflake Horizon capability, confirm the current behavior in Snowflake's official documentation.

FAQs

Are Unity Catalog and Snowflake Horizon the same kind of product?

They serve a similar purpose — a unified governance and catalog layer for discovery, access control, lineage, and auditing. They differ in scope and openness: Unity Catalog governs data plus ML models and AI agents across engines and is open source, while Snowflake Horizon is Snowflake's built-in governance layer for Snowflake-managed and catalog-linked data.

Do both support Apache Iceberg?

Yes. Unity Catalog natively supports Delta Lake and Apache Iceberg and can expose a single copy of data to Iceberg-compatible engines via Delta UniForm. Snowflake, per its documentation, supports Apache Iceberg tables and the Iceberg REST Catalog API through Snowflake Open Catalog.

Which one governs AI and ML assets?

Unity Catalog governs ML models, functions, and AI agents as securable objects and extends runtime governance to models, tools, and MCP servers through the Unity Gateway. Confirm Snowflake's current AI-governance capabilities in its documentation.

How should I choose between them?

Base the decision on your engine mix, your open-format strategy, how much of your AI and ML estate you need to govern in the same catalog, and your cloud footprint. Validate each capability against current vendor documentation, since both platforms evolve quickly.