Microsoft Fabric Updates Blog

Fabric Data Warehouse goes all-in on enterprises at Ignite

Additional Authors: Peri Rocha, Ancy Philip, Jovan Popovic, and Twinkle Cyril  

Fabric Data Warehouse represents a bold, ultra-modern evolution in data warehousing, purpose-built for the demands of the 2020s. By following in the footsteps of industry giants, we’ve had the unique advantage of learning from their successes and challenges—allowing us to deliver a solution that’s both innovative and robust.

Just two years out of General Availability, Fabric Data Warehouse is already delivering standout performance, scalability, ease of use, and cost benefits as part of the broader Fabric platform. Today, we’re announcing major new capabilities to accelerate your data journey.

Clustering

Data Clustering (Preview) unlocks significant performance gains and reduced consumption use for queries. By organizing rows with similar values together during ingestion, Data Clustering enables aggressive file pruning, only scanning files with data that match query predicates.

This optimization is powered by a sophisticated algorithm that preserves data locality across multiple dimensions, outperforming traditional techniques like lexicographical indexes. For more information about Data Clustering in Fabric Data Warehouse, refer to the documentation on Data clustering in Fabric Data Warehouse.

Identity Columns

We’re excited to announce IDENTITY columns (Preview) in Fabric Data Warehouse, a long-awaited feature that simplifies surrogate key generation during data ingestion. IDENTITY columns automatically produce unique values for each new row, eliminating the need for manual key assignments and eliminating the risk of key duplication and key integrity issues.

This system-managed approach ensures uniqueness across the Fabric Warehouse distributed engine, even when separate data ingestion jobs start in parallel. For more information about IDENTITY columns in Fabric Data Warehouse, refer to the documentation.

VARCHAR(MAX) support

Fabric Data Warehouse and SQL analytics endpoints for mirrored items now support large string and binary data using VARCHAR(MAX) and VARBINARY(MAX) types.

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Data Warehouse lets you ingest, store, process, and analyze large descriptive text, logs, JSON, or spatial data, with up to 16MB per cell, without hitting the size limits for most of the data that is common in the warehouse scenarios.

The SQL endpoint for mirrored items ensures large values from source systems are read without the previous 8KB truncation. For new tables, string and binary delta types are mapped to varchar(max) and varbinary(max) SQL types in SQL analytics endpoint. Existing tables with columns already storing large objects can be recreated to adopt the new data type or will be automatically upgraded to VARCHAR(MAX) on the next schema change. This is critical for preventing JSON corruption in mirrored Cosmos DB artifacts, where truncation could break queries due to malformed JSON.

Warehouse Snapshots (Generally Available)

Managing data consistency during ETL has always been a challenge for our customers. Dashboards break, KPIs fluctuate, and compliance audits become painful when reporting hits ‘half-loaded’ data. With Warehouse Snapshots, Microsoft Fabric solves this by giving you a stable, read-only view of your warehouse at a specific point in time. Think of this as a true time travel database, an industry-first capability that sets us apart.

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For more information on Warehouse Snapshots in Fabric Data Warehouse, refer to the full blog post on Warehouse Snapshots in Microsoft Fabric (Generally Available)

Unlock the Future of Analytics Today

Step into a new era where analytics, AI, and innovation converge. Fabric Warehouse isn’t just another platform—it’s the only solution that delivers end-to-end power: ETL, SQL, BI, AI, Agents, and even operational apps—all in one seamless experience.

Start strong with the industry’s most generous offering – 60 days free, then keep building for just $0.36 an hour—less than your daily coffee. Imagine what you can achieve in minutes, hours, weeks, and months with this kind of capability at your fingertips.

Ready to transform your data game?

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