A Microsoft Fabric proof of concept can be built quickly, but designing a Fabric platform for production requires decisions about far more than individual workloads. This session presents an end-to-end architecture for moving data from source systems through ingestion, OneLake, data engineering and warehousing, semantic models, and Power BI. We will examine workspace design, data organization, security, governance, performance, workload boundaries, and operational considerations. Rather than treating Fabric services as isolated features, attendees will learn how the pieces fit together as one enterprise analytics platform and how to make practical architecture decisions that remain maintainable as data volume, teams, and use cases grow.

Focus: End-to-end Fabric architecture: ingestion, OneLake, engineering, warehouse/lakehouse, semantic models, Power BI, governance, and operational design.

Objectives:
1. Design an end-to-end Fabric architecture from ingestion and OneLake through analytics and Power BI.
2. Choose appropriately between Fabric Data Engineering, Data Factory, Lakehouse, and Warehouse patterns based on workload requirements.
3. Apply production architecture principles for security, governance, performance, workspace organization, and maintainability.