Databricks Unity Catalog vs Snowflake Horizon: Data Governance Compared

Introduction

Data governance used to mean creating a 200-page policy document that nobody read and a data steward role that nobody wanted. That has changed. Modern governance is about enforcing access controls, tracking lineage, monitoring quality, and enabling safe data sharing, all through tools built into the platforms you already use. The two biggest names in this space are Databricks Unity Catalog and Snowflake Horizon. Both are mature, both are opinionated, and both want to be your single governance layer. This comparison breaks down where each one excels, where it falls short, and how to choose between them. For deeper dives into each platform's governance model, see our guides on Unity Catalog and Snowflake governance best practices.

The Big Picture: Philosophy and Architecture

Before we get into feature comparisons, it helps to understand the architectural philosophy behind each tool, because that philosophy shapes everything.

Unity Catalog is built around openness and multi-engine access. Databricks open-sourced it in mid-2024, and it supports the Iceberg REST Catalog API, which means engines outside Databricks (Spark, Trino, Flink, DuckDB) can read tables registered in Unity Catalog. It uses a three-level namespace: catalog.schema.table. It is actively replacing the legacy Hive Metastore, with Databricks disabling Hive Metastore features over time.

Snowflake Horizon is built around Snowflake's walled-garden approach. Everything happens inside Snowflake. Governance features are deeply integrated with the query engine, which means they work seamlessly but only within the Snowflake ecosystem. It uses Snowflake's existing database.schema.table namespace. Features like Secure Data Sharing, dynamic data masking, and row access policies are native to the platform, not bolted on.

The core tension: Unity Catalog optimizes for a multi-platform world. Snowflake Horizon optimizes for a Snowflake-first world. Your architecture determines which philosophy fits better.

Access Control: Who Can See What

This is the heart of data governance. Both platforms take different approaches.

Unity Catalog: Attribute-Based Access Control

Unity Catalog supports attribute-based access control (ABAC), which lets you define permissions based on attributes of the user, the data, and the context. For example, you can create a policy that says "users in the analytics team can read tables tagged as 'non-sensitive' in any schema within the production catalog." This scales much better than traditional role-based approaches when you have hundreds of tables and dozens of teams.

Unity Catalog also supports standard SQL GRANT/REVOKE commands, row-level security through row filters, and column masking. Permissions are centralized across all Databricks workspaces, so a policy set in one workspace applies everywhere.

Snowflake Horizon: Row and Column Security

Snowflake Horizon offers row access policies, column-level masking policies, and dynamic data masking. These are defined as SQL objects and applied to tables or views. When a user queries a table, Snowflake evaluates the policy in real time and returns only the rows and columns the user is allowed to see.

Snowflake also provides object tagging, where you tag tables and columns with classifications (like "PII", "confidential", or "public") and then define masking policies based on tags. This is similar to ABAC in concept, though the implementation is Snowflake-specific.

Data Lineage: Tracking Where Data Comes From

Lineage is critical for debugging pipeline issues, impact analysis, and compliance. Both platforms offer automated lineage tracking, but the scope differs.

Unity Catalog captures lineage automatically for all Databricks workloads: Spark jobs, SQL queries, Delta Live Tables, and notebook executions. It tracks column-level lineage (which source columns feed which target columns), table-level lineage (dependencies between tables), and notebook/job lineage (which code produced which tables). This lineage is queryable through APIs and visible in the Databricks UI.

Snowflake Horizon tracks lineage through its ACCESS_HISTORY view, which logs every column read and write operation. It also tracks object dependencies (views depending on tables, stored procedures referencing tables). Snowflake's lineage is particularly strong for understanding who accessed what data and when, which is valuable for compliance audits. For our full take on Snowflake governance workflows, see our Snowflake governance guide.

The practical difference: Unity Catalog gives you richer lineage within the Databricks ecosystem (especially with DLT). Snowflake gives you stronger access audit trails. If your primary concern is "who looked at this PII data last month," Snowflake's access history is excellent. If your primary concern is "which downstream models break if I change this column," Unity Catalog's column-level lineage is more useful.

Data Quality

Both platforms have invested heavily in built-in data quality monitoring.

Unity Catalog integrates with Databricks Lakehouse Monitoring, which profiles tables, detects anomalies in volume and distribution, and tracks data quality metrics over time. Delta Live Tables adds expectations (constraints that validate data during pipeline execution). Quality metrics are stored as Delta tables themselves, so you can build dashboards and alerting on top of them.

Snowflake Horizon includes data quality monitoring through its data metric functions. You can define custom quality checks (null rates, uniqueness, freshness) and attach them to tables. Snowflake evaluates these checks on a schedule and stores results in system views. It also integrates with external quality tools through the Snowflake Marketplace.

Both approaches work well. Unity Catalog's quality integration is tighter if you use DLT (expectations are part of your pipeline definition). Snowflake's approach is more flexible for teams that use multiple tools for quality monitoring.

Data Sharing

This is where Snowflake Horizon has a clear advantage.

Snowflake Secure Data Sharing lets you share live data between Snowflake accounts without copying it. The consumer queries the provider's data directly. No ETL, no sync jobs, no stale copies. It works across Snowflake accounts, across regions, and across cloud providers. The provider controls access, and all governance policies (masking, row security) apply to shared data. This is genuinely transformative for organizations that share data with partners, vendors, or subsidiaries.

Unity Catalog's sharing works through Delta Sharing, an open protocol for sharing data across organizations and platforms. Delta Sharing supports sharing with non-Databricks consumers (any client that speaks the Delta Sharing protocol can read the data). It is more open than Snowflake's approach, but the consumer experience is not as seamless. Setting up a Delta Sharing recipient requires more configuration than Snowflake's one-click sharing.

If data sharing is a core use case for your organization, Snowflake's approach is more mature and easier to operationalize. If you need to share with consumers who are not on Snowflake, Delta Sharing's openness is an advantage.

Multi-Engine and Open Format Support

This is where Unity Catalog pulls ahead.

Unity Catalog supports the Iceberg REST Catalog API, which means any engine that speaks this API (Spark, Trino, Flink, Dremio, Starburst, DuckDB) can discover and query tables registered in Unity Catalog. Your governance policies travel with the data, regardless of which engine reads it. This is a big deal for organizations that use multiple query engines or want to avoid vendor lock-in.

Snowflake Horizon, by design, only governs data within Snowflake. If you need to query the same data from Spark or Trino, you are outside Snowflake's governance boundary. Snowflake does support Iceberg Tables (reading and writing Iceberg format), which creates an escape hatch, but the governance policies do not extend to external engines reading those files. For a broader comparison of these two platforms, see our Databricks vs Snowflake comparison.

Migration Considerations

If you are already on one platform and thinking about governance, here is what the migration path looks like.

Migrating to Unity Catalog (from Hive Metastore): Databricks is actively migrating away from Hive Metastore, with legacy features being disabled over time. The migration involves upgrading your workspace to Unity Catalog, registering existing tables, setting up the three-level namespace, and migrating access controls from Hive-style GRANT statements to Unity Catalog's model. Databricks provides automated migration tools, but plan for testing. Any external tools that connect to Hive Metastore directly will need to be updated to use Unity Catalog's APIs or the Iceberg REST Catalog.

Setting up Snowflake Horizon: If you are already on Snowflake Enterprise Edition or above, Horizon features are available immediately. There is no migration per se. You start by tagging sensitive objects, creating masking policies, and enabling access history. The learning curve is gentle because everything is SQL-based and uses familiar Snowflake objects.

Cost Comparison

Neither platform charges separately for governance features. The cost is baked into your platform spend.

• Unity Catalog: Included with Databricks. No additional per-feature charge. You pay for compute and storage as usual. The open-source version is free but requires self-managed infrastructure.
• Snowflake Horizon: Most governance features require Enterprise Edition or above. If you are on Standard Edition, upgrading to Enterprise is the main cost. Once on Enterprise, features like dynamic masking, tagging, and access history are included.

The real cost is engineering time. Setting up governance properly (defining policies, tagging data, configuring access controls, testing) takes weeks to months depending on the size of your data estate. Both platforms require similar effort in this regard.

When to Use Which

Choose Unity Catalog if:

• You run Databricks as your primary analytics platform
• You need multi-engine access to governed data (Spark, Trino, Flink)
• Open source and avoiding vendor lock-in are priorities
• You have complex access control requirements that benefit from ABAC
• You are migrating off Hive Metastore (you will need to move to Unity Catalog eventually)

Choose Snowflake Horizon if:

• Snowflake is your primary data warehouse
• Data sharing with external organizations is a key use case
• You want governance that works out of the box with minimal configuration
• Your team prefers SQL-based governance definitions
• Compliance auditing (who accessed what, when) is a primary requirement

Use both if:

• Your architecture includes both Databricks and Snowflake (common in large enterprises)
• You use Databricks for data science and ML while Snowflake serves your BI and reporting teams
• You are willing to invest in a policy management layer that synchronizes rules across both platforms

For teams running both platforms, our data engineering team can help you design a governance architecture that works across your entire stack.

Key Takeaways

• Unity Catalog is open source, supports the Iceberg REST Catalog API, and excels at multi-engine governance with attribute-based access control
• Snowflake Horizon excels at built-in data sharing (Secure Data Sharing with zero copies), dynamic data masking, and compliance audit trails
• Databricks was named a leader in the IDC MarketScape for AI Governance 2025 to 2026
• Unity Catalog is actively replacing Hive Metastore. If you are on Databricks, plan your migration now
• Snowflake Horizon requires Enterprise Edition or above for most governance features
• Both platforms include governance at no additional per-feature cost. The real cost is engineering time for setup and policy definition
• Many enterprises use both. The challenge is maintaining consistent policies across platforms