Databricks Data Federation Arrives

Databricks Lakehouse Federation lets users query data wherever it lives, unifying access and governance across disparate sources without migration.

Daniel Singer

Editor, StartupHub.ai

Jun 13 at 5:01 AMUpdated Aug 22, 20263 min read

Databricks Lakehouse Federation diagram showing data sources connecting to Unity Catalog and Genie — Databricks Lakehouse Federation connects disparate data sources under a unified governance layer.

Visual TL;DR

Siloed Data ProblemDriver

businesses want to ask complex questions across scattered systems

From the articleThe era of siloed data may be drawing to a close.

Agentic AI DemandDriver

driving need for cross-source reasoning and immediate answers

From the article 2 mentionsThis new capability addresses the growing demand for cross-source reasoning, particularly driven by agentic AI.

Databricks Lakehouse FederationCore

connects directly to existing data sources without migration

From the article 5 mentionsDatabricks has introduced Databricks Lakehouse Federation, a feature designed to let users access and query data wherever it resides, eliminating the need for immediate, large-scale migrations.

Query Data WhereverEffect

From the article 2 mentionsDatabricks has introduced Databricks Lakehouse Federation, a feature designed to let users access and query data wherever it resides, eliminating the need for immediate, large-scale migrations.

Unified Data AccessOutcome

consistent permissions, lineage, and access controls across sources

From the article 6 mentionsIt’s a significant step towards truly unified data management.

Future EnhancementsContext

ongoing development for even broader data source integration

Leveraging Existing ContextContext

brings disparate sources under Unity Catalog for governance

Enterprise-Grade SecurityOutcome

From the articleThis ensures consistent permissions, lineage, and access controls, offering enterprise-grade security without rebuilding infrastructure source by source.

Contents(3)

The era of siloed data may be drawing to a close. Databricks has introduced Databricks Lakehouse Federation, a feature designed to let users access and query data wherever it resides, eliminating the need for immediate, large-scale migrations.

This new capability addresses the growing demand for cross-source reasoning, particularly driven by agentic AI. Businesses now want to ask complex questions like "which marketing campaigns drove the most ROI last quarter?" and receive immediate answers, even when that data is scattered across systems like AWS Glue, Snowflake, Oracle, or BigQuery.

Databricks Lakehouse Federation connects directly to these existing data sources, bringing them under the umbrella of Unity Catalog. This ensures consistent permissions, lineage, and access controls, offering enterprise-grade security without rebuilding infrastructure source by source. It’s a significant step towards truly unified data management.

Connecting Without Copying

The core of Lakehouse Federation lies in its ability to connect to external data sources and govern them alongside native data within Databricks. This allows tools like Genie, Databricks' conversational AI interface, to access an extended data estate on demand.

The process involves creating a connection to an external source, such as an AWS Glue database, and then syncing its metadata into Unity Catalog. This provides access to tables without physically copying the data, keeping it up-to-date and minimizing disruption to source systems.

Leveraging Existing Context

Raw table and column names often lack the necessary context for AI models. Databricks Lakehouse Federation now automatically pulls in existing metadata, such as table descriptions and column comments, from sources like Glue and BigQuery. This preserves valuable business context, allowing AI tools to understand schemas more effectively.

Furthermore, Databricks is enabling the definition of reusable semantics on top of federated data. This means business logic, like calculating ROI, can be defined once in Unity Catalog and consistently applied across all tools and queries, whether they access federated or managed data. This capability is crucial for deriving trusted, identical calculations from disparate datasets.

This approach to managing data across multiple locations is a key development, building on Databricks' efforts to unify data governance. The ability to query federated data in natural language is particularly impactful for users who previously struggled with fragmented information, akin to the challenges highlighted in discussions about the US Gov's Data Dilemma.

Future Enhancements

Databricks plans to further enhance Lakehouse Federation with richer business semantics for federated tables, AI-powered metadata augmentation, and expanded support for more catalogs and platforms. Users can also opt to upgrade federated tables to Unity Catalog managed tables for significant performance and cost improvements.

This move positions Databricks to better compete in the evolving data landscape, where seamless access and intelligent analysis of distributed data are paramount. It builds on previous efforts like Databricks, BigQuery Unite Data Catalogs, signifying a broader trend towards interoperability.

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#Databricks #Lakehouse #Data Federation #Data Governance #Unity Catalog #AI #Genie

Written by

Daniel Singer

Editor, StartupHub.ai

Daniel Singer is the editor of StartupHub.ai, a technology expert and thought leader on AI and its applications across sectors, from fintech and healthcare to developer tooling and consumer software. He writes and tests the tools covered here thoroughly and regularly, and built StartupHub.ai to give founders, operators and buyers a clearer read on what they are actually being sold.