Evaluate lakehouse, warehouse, and eventhouse options in Microsoft Fabric to select the appropriate analytical data store for different business scenarios based on data format, query language, write patterns, and team skills.
By the end of this module, you're able to:
- Describe analytical data store options in Microsoft Fabric.
- Evaluate lakehouse, warehouse, and eventhouse capabilities.
- Choose the appropriate data store for a business scenario.
Learn dimensional schema types, fact and dimension table design, and slowly changing dimension patterns for analytics workloads in Microsoft Fabric.
By the end of this module, you'll be able to:
- Describe dimensional schema types for analytics
- Design fact tables for business processes
- Design dimension tables for descriptive attributes
- Implement slowly changing dimension patterns
Apply low-code transformations using Power Query in Dataflows Gen2 to prepare analytical data for downstream consumption.
By the end of this module, you'll be able to:
- Create and configure Dataflows Gen2 in Microsoft Fabric
- Apply Power Query transformations to prepare data
- Optimize dataflow performance with query folding
- Load transformed data to lakehouse or warehouse destinations
Use Fabric notebooks to transform data with Spark SQL and PySpark, connecting to lakehouses, warehouses, and other data stores.
By the end of this module, you'll be able to:
- Describe notebooks in Microsoft Fabric
- Shape and clean data using Spark SQL and PySpark
- Combine and aggregate data using Spark SQL and PySpark
- Write and size Delta tables appropriately
Use T-SQL in Microsoft Fabric warehouses to transform and query data, create reusable views and stored procedures, and build dimensional tables.
By the end of this module, you'll be able to:
- Transform warehouse data using T-SQL queries
- Create views for reusable transformation logic
- Build stored procedures for repeatable data processing
- Implement dimensional tables in a warehouse

Syllabus

Choose data stores in Microsoft Fabric
- Introduction
- Describe analytical data store options
- Evaluate lakehouse capabilities
- Evaluate warehouse capabilities
- Evaluate eventhouse capabilities
- Case study - Choose data stores for an integrated analytics solution
- Module assessment
- Summary
Design dimensional models for analytics in Microsoft Fabric
- Introduction
- Describe dimensional schema types
- Design fact tables
- Design dimension tables
- Implement slowly changing dimensions
- Exercise: Design and implement a dimensional model
- Knowledge check
- Summary
Transform data using Dataflows Gen2 in Microsoft Fabric
- Introduction
- Understand Dataflows Gen2
- Transform data with Power Query
- Optimize Dataflows Gen2 performance
- Exercise: Transform data with Dataflows Gen2
- Knowledge check
- Summary
Transform data using notebooks in Microsoft Fabric
- Introduction
- Describe notebooks in Fabric
- Shape and clean data
- Combine and aggregate data
- Write and size Delta tables
- Exercise: Transform data with notebooks
- Knowledge check
- Summary
Transform data using T-SQL in Microsoft Fabric
- Introduction
- Transform data with T-SQL queries
- Create views for reusable logic
- Build stored procedures
- Implement dimensional tables
- Exercise: Transform data with T-SQL
- Knowledge check
- Summary