Microsoft Fabric: what does it actually change for BI teams?

Fabric in one sentence

Fabric is Microsoft's unified analytics platform that brings together in one environment a Lakehouse, Data Warehouse, Dataflow Gen2, Notebooks, and Power BI. But for established BI teams, the key question is: what does it actually change in your day-to-day operations?

Fabric's key promise is unification. No more juggling between Azure Data Factory, Azure SQL, Power BI Service, and Synapse. One tenant, one security model, one capacity pool. For teams managing complex BI estates, this simplification is the primary selling point.

What Fabric actually changes

For ETL/SSIS teams

Dataflow Gen2 is the cloud replacement for SSIS within the Microsoft ecosystem. It runs on a Spark engine, supports Power Query M and SQL, and integrates natively with the Fabric Lakehouse and Warehouse.

The SSIS → Dataflow Gen2 transition is not automatic. Dataflow Gen2 does not support the full SSIS feature set — Script Tasks, custom components, and advanced error handling require rethinking.

For Power BI teams

The most significant change is the DirectLake mode for Power BI. Instead of importing data into the Power BI model (Import mode) or querying sources live (DirectQuery), DirectLake reads Parquet files directly from the Lakehouse. The result: import-like performance with near-real-time freshness.

For data engineering teams

The Fabric Lakehouse replaces the Azure Data Lake + Synapse combination. It combines unstructured file storage with a relational SQL endpoint — no need to maintain two separate services.

What Fabric does not solve

Fabric is not a magic button. Migrating to Fabric does not automatically resolve the technical debt accumulated in your SSIS packages or Power BI models. If your data flows are undocumented and your reports are unmaintained, moving to Fabric just moves the problem to the cloud.

Fabric also does not resolve the vendor lock-in concern. Your entire data stack runs in a single Microsoft environment. For some organisations, this is a feature. For others, it is a risk.

Finally, Fabric's capacity-based cost model (CU — Capacity Units) requires careful sizing. Overprovisioning wastes budget; underprovisioning causes throttling and poor performance.

Fabric vs Snowflake: summary table

Fabric suits organisations already in the Microsoft ecosystem that want to consolidate their data stack. If your teams use Power BI daily and your data is on Azure, Fabric is a natural evolution.

Our recommendation

Don't choose Fabric because Microsoft says so. Choose it if it genuinely simplifies your architecture and your teams are ready to adopt the new paradigm.