As organizations mature their Microsoft Fabric platform, standard reports and curated semantic models are often no longer enough. Business teams want greater flexibility. They want to build their own semantic models, create departmental data products, and work directly with curated data. The challenge is enabling that freedom without compromising governance, security, or the integrity of your central platform.

This session is based on a real-world Fabric implementation where we built a fully automated, metadata-driven solution that provisions and maintains departmental workspaces. Instead of granting users direct access to central lakehouses, the solution creates dedicated workspaces with the correct lakehouses, shortcuts, permissions, security groups, and settings, all driven by metadata and kept continuously in sync.

We'll explore the overall architecture before diving into the implementation. You'll see how metadata defines the desired environment, how Spark and the Fabric REST APIs compare the desired state with the current tenant configuration, and how the solution automatically creates or updates workspaces, shortcuts, permissions, and configuration whenever changes are required.

Along the way we'll discuss the required API permissions, service principal considerations, common pitfalls, and the lessons learned from implementing the solution in production. Although our implementation is Spark-based, the concepts apply equally well to any technology capable of calling the Fabric APIs.

After this session you'll understand how to automate secure data delivery in Microsoft Fabric, how to build a scalable metadata-driven provisioning solution, which Fabric APIs are involved, and how to give business users the flexibility they need without sacrificing governance or control.