22-25 April 2026 
Video unavailable
SQLBits 2026
Stop Guessing: A Decision Framework for Fabric Tool Selection
Fabric offers multiple ways to store and process data—but which should you choose? This session provides a practical decision framework for selecting between Lakehouse, Warehouse, Eventhouse, ingestion patterns, and transformation tools based on your needs.
Microsoft Fabric gives you many ways to accomplish the same goal: ingest data through mirroring, shortcuts, or pipelines; store it in a Lakehouse, Warehouse, or Eventhouse; transform it with Data Factory, Notebooks, or SQL views. But how do you know which option is right for your scenario?
This session cuts through the confusion by providing a practical decision framework for navigating Fabric's component ecosystem. Rather than explaining what each tool does (you can read the docs for that), we'll focus on WHEN and WHY to choose one approach over another.
THE SESSION COVERS:
Data storage decisions: When to use Lakehouse vs. Warehouse vs. Eventhouse vs. sticking with traditional SQL databases or Cosmos DB. We'll explore the tradeoffs in query performance, data engineering flexibility, cost, and governance.
Ingestion pattern comparison: Evaluate mirroring, shortcuts, virtual tables, and delta parquet options. Understand which patterns work best for different source systems, latency requirements, and data volumes.
Transformation tool selection: Compare Data Factory, Notebooks, Data Pipeline Gen2, and SQL views for data transformation. Learn when low-code solutions make sense vs. when you need the power of Spark or SQL.
Real-world scenarios: We'll walk through practical examples from different industries and use cases, mapping business requirements to specific Fabric component choices.
Common decision points and tradeoffs: Understand the implications of your choices on performance, cost, maintainability, skill requirements, and future flexibility.
Attendees are encouraged to share examples from their own organizations to help connect the framework to real-world needs and discuss edge cases together.
WHO SHOULD ATTEND: Data architects, platform engineers, BI developers, and technical decision-makers who need to design Fabric solutions or advise teams on component selection.
WHAT YOU'LL LEAVE WITH: A clear mental model for evaluating Fabric components, confidence in making architectural decisions, and practical criteria you can apply immediately when planning your next Fabric implementation.
This session cuts through the confusion by providing a practical decision framework for navigating Fabric's component ecosystem. Rather than explaining what each tool does (you can read the docs for that), we'll focus on WHEN and WHY to choose one approach over another.
THE SESSION COVERS:
Data storage decisions: When to use Lakehouse vs. Warehouse vs. Eventhouse vs. sticking with traditional SQL databases or Cosmos DB. We'll explore the tradeoffs in query performance, data engineering flexibility, cost, and governance.
Ingestion pattern comparison: Evaluate mirroring, shortcuts, virtual tables, and delta parquet options. Understand which patterns work best for different source systems, latency requirements, and data volumes.
Transformation tool selection: Compare Data Factory, Notebooks, Data Pipeline Gen2, and SQL views for data transformation. Learn when low-code solutions make sense vs. when you need the power of Spark or SQL.
Real-world scenarios: We'll walk through practical examples from different industries and use cases, mapping business requirements to specific Fabric component choices.
Common decision points and tradeoffs: Understand the implications of your choices on performance, cost, maintainability, skill requirements, and future flexibility.
Attendees are encouraged to share examples from their own organizations to help connect the framework to real-world needs and discuss edge cases together.
WHO SHOULD ATTEND: Data architects, platform engineers, BI developers, and technical decision-makers who need to design Fabric solutions or advise teams on component selection.
WHAT YOU'LL LEAVE WITH: A clear mental model for evaluating Fabric components, confidence in making architectural decisions, and practical criteria you can apply immediately when planning your next Fabric implementation.