If you haven’t already, check out Arun Ulag’s hero blog “FabCon and SQLCon 2026: Unifying databases and Fabric on a single, complete platform” for a complete look at all of our FabCon and SQLCon announcements across both Fabric and our database offerings.
The enterprise-grade JDBC driver enables secure, flexible, and performant connectivity to Spark SQL workloads running in Microsoft Fabric, using Fabric’s Livy APIs as the execution layer.
Why this matters
JDBC is a widely used standard for connecting Java applications to data platforms. As Apache Spark remains central to large‑scale data processing, organizations need simple, reliable ways to integrate Spark workloads with existing applications and BI tools.
The Microsoft JDBC Driver for Microsoft Fabric Data Engineering meets this need by enabling developers, data engineers, and administrators to access Fabric Spark workloads through the familiar JDBC interface, while preserving enterprise‑grade security and governance.
Now, the driver is production‑ready and can be confidently used for mission‑critical, enterprise‑scale data engineering workloads in Microsoft Fabric.
The following example provides a quick demonstration of how to set up this driver in DbVisualizer and run a Spark SQL query against your lakehouse data:
Figure: The DbVisualizer main menu, highlighting options for managing databases, creating connections, installing a demo database, and customizing the application.
Deep integration with Fabric Data Engineering
The JDBC driver is purpose‑built for Fabric Data Engineering, offering deep integration with key Fabric capabilities, including:
- Native access to Lakehouse data in OneLake.
- Support for executing jobs using Fabric Environment items.
- Ability to apply custom Spark configurations tailored to different workloads.
- Secure execution via Microsoft Fabric Livy APIs.
This ensures that JDBC‑based tools and applications feel like first‑class citizens in the Fabric ecosystem.
Key features
This release includes all preview capabilities, with additional polish and supportability:
- JDBC 4.2 compliant: Works out‑of‑the‑box with Java 11, 17, and 21, and supports popular tools such as DbVisualizer, DBeaver, Tableau, and Power BI (via JDBC).
- Enterprise authentication: Supports multiple Microsoft Entra ID authentication flows, including interactive browser, client credentials, certificate‑based authentication, and access tokens.
- Robust connection pooling: Built‑in pooling with health monitoring, automatic recovery, and HikariCP integration for high‑throughput workloads.
- Native Spark SQL support: Execute Spark SQL statements directly, with comprehensive support for Spark SQL data types, including complex types such as ARRAY, MAP, and STRUCT.
- Performance and resilience: Asynchronous result‑set prefetching, circuit breaker patterns, automatic reconnection, and advanced retry logic help keep applications running smoothly.
- Proxy and logging support: Full HTTP/SOCKS proxy support and configurable logging for enterprise environments.
We’ll continue to invest in performance, security, and developer experience enhancements as Fabric Data Engineering evolves.
We invite you to start using this release today and continue sharing feedback as we build the future of enterprise Spark connectivity in Microsoft Fabric.
To learn more and get started using the Fabric JDBC Driver, please visit: .
Related blog posts
Microsoft JDBC Driver for Microsoft Fabric Data Engineering (Generally Available)
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Fabric notebooks now support lakehouse auto-binding when used with Git. It is designed to simplify multi-environment workflows and reduce the operational overhead of managing lakehouse references across development, test, and production workspaces.
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