Governing Your Lakehouse: Data Catalog Tools That Work With Dremio

A lakehouse without governance is a liability. Sure, you can query it, but can you trust it? Analysts find tables with no owner, no description, and no clear indication of whether what they're looking at is current. Likewise, compliance teams can't demonstrate data lineage and engineers can't assess the impact of a schema change before they make it.

Dremio's Open Catalog and AI Semantic Layer handles this problem natively: access controls, metric definitions, and catalog metadata are all managed within the platform. For many organisations, that's sufficient. For others, particularly those running enterprise governance programmes across multiple platforms, Dremio needs to fit into a broader data governance ecosystem. This blog summarises some tools that each integrate with Dremio to extend its governance capabilities in different ways.

Alation: Cataloguing Dremio Assets for Data Discovery

Alation connects to Dremio via its Open Connector Framework (OCF), a dedicated integration that automatically extracts metadata from Dremio Software environments. Once connected, Alation ingests schemas, tables, columns, and views from Dremio and surfaces them in its catalogue interface alongside assets from other platforms. Data stewards can add descriptions, trust flags, and endorsements. Analysts can search for Dremio assets by name, tag, or business term without knowing where in the platform the data lives.

The integration is particularly useful in organisations where Dremio manages complex federated data products that draw from multiple underlying sources. Alation's behavioural analytics layer analyses query logs to surface the most frequently accessed tables and columns, giving data teams a usage-based picture of what's actually valuable rather than relying on self-reported documentation. Authentication uses a Personal Access Token, and installation is managed through Alation's Connector Manager. It is worth noting that the current connector supports Dremio Software 24.3.0 and later; with Dremio Cloud support on the roadmap. Dremio's own integration walkthrough is at dremio.com/blog/simplifying-data-discovery-with-the-dremio-connector-for-alation/.

Microsoft OneLake: Bringing Dremio Data Into the Fabric Governance Layer

Microsoft Fabric's Mirrored Dremio Catalog is a zero-copy integration that makes Dremio-managed Iceberg tables visible inside OneLake without moving any data. You select the Dremio tables you want to surface, and Fabric creates metadata references using Iceberg shortcuts. From that point, those tables are queryable via Power BI, the Fabric SQL analytics endpoint, and other Fabric experiences, while the data itself stays in Dremio's storage.

For organisations running Microsoft Fabric as their primary analytics platform, this integration resolves a practical friction point: Dremio holds the governed, well-structured lakehouse data, and Fabric's users need access to it without duplicating it or building pipelines. Governance policies configured in Dremio's Open Catalog continue to apply at query time, so the access controls don't need to be recreated in Fabric. The integration is currently in preview. Dremio's OneLake connector documentation is at docs.dremio.com/current/data-sources/lakehouse-catalogs/onelake/.

Databricks Unity Catalog: Federated Queries Across Catalogs

Dremio connects to Databricks Unity Catalog via its Iceberg REST Catalog connector. In the reverse of the other tools discussed here, this grants Dremio direct read access to Unity Catalog-managed tables. In practice, this means a data team running Databricks for transformation workloads and Dremio for analytics can query Unity Catalog tables from Dremio's SQL interface without duplicating data or maintaining separate access credentials for each platform. Write operations continue through Databricks; Dremio operates in read-only mode against Unity Catalog assets. This is the set-up used by Kion Group, the largest manufacturer of forklift trucks and warehouse equipment in Europe.

This integration is valuable in hybrid lakehouse environments where Databricks and Dremio coexist. Unity Catalog's governance policies cover data written by Databricks workflows, while Dremio's governance layer covers the rest of the lakehouse. Analysts querying across both through Dremio's federated interface get consistent results without needing to know which platform manages which table. Dremio's Unity Catalog documentation is at docs.dremio.com/current/data-sources/lakehouse-catalogs/unity/.

Choosing the Right Combination

The right answer depends on what your organisation already runs. If you're a Microsoft Fabric shop, the OneLake integration is the obvious starting point. If Databricks handles your transformation layer, the Unity Catalog connector fills the cross-platform query gap. And if you need a standalone enterprise catalogue that surfaces Dremio assets alongside everything else in your data estate, Alation is the most direct path today.

In all cases, the underlying principle is the same: Dremio manages access, acceleration, and semantic consistency at query time, while the external catalogue provides the discovery, stewardship, and lineage layer that sits above it. The two layers complement each other rather than duplicate.

If you want to explore how Dremio fits into a governed lakehouse architecture, a free environment at dremio.com/get-started gives you Open Catalog and the AI Semantic Layer from day one to build on.