- Module 1: Choose a Power BI model framework
By the end of this module, you’ll be able to:
- Describe Power BI model fundamentals.
- Determine when to develop an import model.
- Determine when to develop a DirectQuery model.
- Determine when to develop a composite model.
- Choose an appropriate Power BI model framework.
- Module 2: Scalable data models enable enterprise-scale analytics in Power BI. Implement data modeling best practices, use large dataset storage format, and practice building a star schema to design analytics solutions that can scale.
By the end of this module, you’ll be able to:
- Describe the importance of building scalable data models
- Implement Power BI data modeling best practices
- Use the Power BI large dataset storage format
- Module 3: Create Power BI transformation logic for reuse across your organization with Power BI dataflows. Learn how to combine Power BI dataflows with Power BI Premium for scalable ETL, and practice creating and consuming dataflows.
By the end of this module, you’ll be able to:
- Describe Power BI dataflows and use cases.
- Describe best practices for implementing Power BI dataflows.
- Create and consume Power BI dataflows.
Overview
Syllabus
- Module 1: Module 1: Choose a Power BI model framework
- Introduction
- Describe Power BI model fundamentals
- Determine when to develop an import model
- Determine when to develop a DirectQuery model
- Determine when to develop a composite model
- Choose a model framework
- Knowledge check
- Summary
- Module 2: Module 2: Understand scalability in Power BI
- Introduction
- Describe the significance of scalable models
- Implement Power BI data modeling best practices
- Configure large datasets
- Exercise: Create a star schema model
- Knowledge check
- Summary
- Module 3: Module 3: Create and manage scalable Power BI dataflows
- Introduction
- Define use cases for dataflows
- Create reusable assets
- Implement best practices
- Exercise: Create a dataflow
- Knowledge check
- Summary
