Mirroring in Microsoft Fabric provides low-cost, low-latency replication of operational data into OneLake, enabling near real-time analytics without complex ingestion pipelines. As the catalog of supported source systems continues to grow, data practitioners can increasingly adopt a OneLake-native approach, treating mirrored data as the bronze layer and focusing engineering effort on creating trusted data products.

With release 5.0 Fabric Accelerator now supports a new OneLake-Native ELT Pattern for Microsoft Fabric Mirrored Databases. Because mirrored data already lands in OneLake, no separate ingestion process is required. Instead, the Fabric Accelerator metadata framework has been enhanced to orchestrate and automate transformations through the silver and gold layers while maintaining the same governance, quality, and engineering rigor as traditional ingestion-based pipelines.

Aligned with Fabric’s One Copy architecture, this pattern enables organizations to build trusted data products directly from shared OneLake assets without re-ingesting or duplicating data. While initially focused on Mirrored Databases, the same approach can be applied to other OneLake-native sources, including Fabric SQL Databases, Lakehouses, Warehouses, Data Mesh domains, and OneLake Shortcuts managed by teams across the organization.

Key Benefits

• Eliminates unnecessary ingestion for OneLake-native sources
• Reduces data duplication and movement
• Accelerates delivery of silver and gold data products
• Enables reuse of shared enterprise data assets
• Extends Fabric’s One Copy principle across data engineering workloads
• Provides a consistent metadata-driven approach regardless of data origin

In addition to Mirroring updates, a few important updates also went live in this release:

• Special thanks to Pardha Saradhi from Data League for creating and contributing the flow diagrams that now form part of the Fabric Accelerator Wiki
• Automation of gold Datawarehouse schema creation.
• Fixed sensitivity-label dialogs from blocking automated database-name retrieval in deployments.
• Performance improvements across all Spark notebooks.

Try out these new capabilities Implementing Fabric Accelerator with Mirrored Databases