Design and transform analytics data in Microsoft Fabric

  • Learning Path
  • 5 Modules

At a glance

Design dimensional models and apply transformations using dataflows, Spark notebooks, and T-SQL to produce consistent, analysis-ready data in Microsoft Fabric.

Prerequisites

  • Experience working with data stores such aslakehouses or warehouses
  • Familiarity with SQL query syntax
  • Understanding of data modeling concepts such as tables, relationships, and keys

Modules in this learning path

Choose data stores in Microsoft Fabric

Evaluate lakehouse, warehouse, and eventhouse options to select the appropriate analytical data store for business scenarios in Microsoft Fabric.

Design dimensional models for analytics in Microsoft Fabric

Learn dimensional schema types, fact and dimension table design, and slowly changing dimension patterns for analytics workloads in Microsoft Fabric.

Transform data using Dataflows Gen2 in Microsoft Fabric

Apply low-code transformations using Power Query in Dataflows Gen2 to prepare analytical data for downstream consumption.

Transform data using notebooks in Microsoft Fabric

Use Fabric notebooks to transform data with Spark SQL and PySpark, connecting to lakehouses, warehouses, and other data stores.

Transform data using T-SQL in Microsoft Fabric

Use T-SQL in Microsoft Fabric warehouses to transform and query data, create reusable views and stored procedures, and build dimensional tables.

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