Unlocking Insights Faster with Microsoft Fabric’s Mirroring Capabilities

Microsoft Fabric’s mirroring feature provides a low-cost, low-latency, and highly integrated solution for unifying your data. It allows you to continuously replicate your existing data from a variety of Azure databases and external sources directly into OneLake, Fabric’s unified data lake.

Once your data is in OneLake, it’s immediately available in a queryable format for all Fabric services. This enables you to perform a wide range of analytical tasks, from data engineering and executing Spark notebooks to building interactive Power BI reports and other business intelligence projects. Mirroring is a turnkey solution designed for openness and collaboration, making it a powerful and easy-to-use product that simplifies your analytics needs.

Why Use Mirroring in Microsoft Fabric?

Mirroring in Fabric breaks down data silos by replicating data and metadata in near real-time into OneLake, making it instantly analytics-ready. It eliminates complex ETL pipelines, reduces delays, and enables seamless access for BI, AI, and data engineering. Built on a SaaS foundation, it simplifies integration, supports familiar tools like SSMS and VS Code, and ensures secure, collaborative data sharing across your organization.

Types of Mirroring in Microsoft Fabric

Microsoft Fabric supports three mirroring approaches to bring data into OneLake efficiently:

1. Database Mirroring
   Replicates full databases and tables into OneLake, enabling seamless integration of data from various sources into a single analytics platform. This supports real-time analytics and simplifies data consolidation.

2. Metadata Mirroring
   Syncs metadata (like catalog names, schemas, and tables) without moving the actual data. Using shortcuts, Fabric allows direct access to source data, reducing duplication while maintaining easy accessibility.

3. Open Mirroring
   Built on the open Delta Lake format, this approach allows developers to write change data directly into mirrored database items in Fabric using public APIs. It offers flexibility for custom applications to integrate with Fabric in a standardized, open way.

What Mirroring Enables

The mirroring feature allows organizations to:

• Access real-time Databricks data in Fabric: Business users can instantly analyze live Databricks datasets in Power BI using Direct Lake mode or SQL queries within Fabric.
• Eliminate data duplication and latency: Because the data remains in Databricks, there is no need for time-consuming and costly ETL jobs or storage replication.
• Maintain governance and security: Unity Catalog’s fine-grained access policies are respected in Fabric, and OneLake’s security framework integrates seamlessly.
• Accelerate collaboration across teams: Data scientists working in Databricks and business analysts working in Power BI can collaborate on the same live datasets, governed under a single control plane.

Key Capabilities and How It Works

Mirroring is simple to configure and operate. Users can mirror entire Unity Catalog catalogs, schemas, or specific tables from Databricks into Microsoft Fabric by providing their Databricks workspace credentials. Once linked, Fabric continuously monitors and reflects metadata updates from Databricks.

The mirrored tables are made available in Fabric’s SQL Analytics Endpoint, meaning users can:

• Query the data using T-SQL in notebooks.
• Build Power BI datasets without importing or copying data.
• Create semantic models that remain connected to the live Databricks backend.

There is no requirement for a live Databricks cluster during query execution, as Fabric reads directly from the storage layer using the mirrored metadata.

Mirroring Azure Databricks Unity Catalog to Microsoft Fabric OneLake

The general availability of Mirroring for Azure Databricks Unity Catalog in Microsoft Fabric enables real-time, secure, and ETL-free access to Databricks-managed datasets directly in OneLake. This integration allows users to:

• Bring Unity Catalog tables into Fabric with just a few clicks—at catalog, schema, or table level.

• Query and analyze data instantly using Power BI Direct Lake, semantic models, and other Fabric tools.

• Avoid data duplication with read-only mirrored tables that automatically stay in sync as data changes in Databricks.

• Leverage open data formats (Delta Parquet) and full interoperability across platforms.

• Ensure enterprise-grade security and governance with OneLake’s integrated access control and compliance features.

• Automate deployment using public APIs for CI/CD and scripting.

This seamless integration eliminates complex ETL pipelines, reduces storage costs, and delivers real-time insights—bridging Azure Databricks and Microsoft Fabric on a modern lakehouse foundation.

What’s New in the GA Release

The general availability (GA) release of Mirroring brings several enterprise-grade enhancements:

• Firewall-enabled ADLS access: Allows organizations to restrict data access to trusted networks, supporting more stringent security postures.
• Public APIs for CI/CD automation: Teams can now programmatically create, manage, and update mirrored catalog items, making it easier to integrate mirroring into DevOps pipelines.
• OneLake security enforcement: Deep integration with OneLake ensures that access controls, authentication, and data masking policies are honoured.

These capabilities make Mirroring production-ready for enterprises requiring high availability, robust governance, and secure collaboration.

What’s Next on the Roadmap

Microsoft has outlined several enhancements coming to Mirroring in future releases:

• Support for Delta Sharing and federated tables: Enabling broader collaboration across external platforms.
• Federated views and streaming datasets: Supporting more complex and dynamic analytics scenarios.
• Policy-aware mirroring: Integrating row-level security (RLS), column-level masking (CLM), and semantic inheritance across platforms.

These features will further strengthen the position of Fabric and Databricks as core components of a unified, enterprise-grade data platform.

Final Thoughts

Mirroring Azure Databricks Unity Catalog into Microsoft Fabric represents a fundamental shift in how organizations access and govern data. It eliminates silos, reduces operational complexity, and enables real-time collaboration across teams without compromising on governance or performance.

At Quadrant Technologies, we help organizations simplify data access and strengthen governance by leveraging the latest innovations across the Microsoft Intelligent Data Platform. To learn more or connect with our experts, contact us at marcomms@quadranttechnologies.com.