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Microsoft

Model data in Power BI

Microsoft via Microsoft Learn

Overview

  • Module 1: The process of creating a complicated data model in Power BI is straightforward. If your data is coming in from more than one transactional system, before you know it, you can have dozens of tables that you have to work with. Building a great data model is about simplifying the disarray. A star schema is one way to simplify a data model, and you’ll learn about the terminology and implementation of them in this module. You will also learn about why choosing the correct data granularity is important for performance and usability of your Power BI reports. Finally, you’ll learn about improving performance with your Power BI data models.
  • In this module, you will:

    • Create common date tables
    • Configure many-to-many relationships
    • Resolve circular relationships
    • Design star schemas
  • Module 2: Data Analysis Expressions (DAX) is a programming language that is used throughout Microsoft Power BI for creating calculated columns, measures, and custom tables. It is a collection of functions, operators, and constants that can be used in a formula, or expression, to calculate and return one or more values. You can use DAX to solve a number of calculations and data analysis problems, which can help you create new information from data that is already in your model.
  • By the end of this module, you'll be able to:

    • Build quick measures.
    • Create calculated columns.
    • Use DAX to build measures.
    • Discover how context affects DAX measures.
    • Use the CALCULATE function to manipulate filters.
    • Implement time intelligence by using DAX.
  • Module 3: Performance optimization, also known as performance tuning, involves making changes to the current state of the data model so that it runs more efficiently. Essentially, when your data model is optimized, it performs better.
  • By the end of this module, you will be able to:

    • Review the performance of measures, relationships, and visuals.
    • Use variables to improve performance and troubleshooting.
    • Improve performance by reducing cardinality levels.
    • Optimize DirectQuery models with table level storage.
    • Create and manage aggregations.

Syllabus

  • Module 1: Design a data model in Power BI 
    • Introduction
    • Work with tables
    • Create a date table
    • Work with dimensions
    • Define data granularity
    • Work with relationships and cardinality
    • Resolve modeling challenges
    • Lab - Model data in Power BI Desktop, Part 1
    • Lab - Model data in Power BI Desktop, Part 2
    • Check your knowledge
    • Summary
  • Module 2: Introduction to creating measures using DAX in Power BI
    • Introduction to DAX
    • Understand context
    • Use the Calculate function
    • Use relationships effectively
    • Create semi-additive measures
    • Lab - Introduction to DAX in Power BI Desktop
    • Work with time intelligence
    • Lab - Time Intelligence and Measures in DAX
    • Check your knowledge
    • Summary
  • Module 3: Optimize a model for performance in Power BI 
    • Introduction to performance optimization
    • Review performance of measures, relationships, and visuals
    • Use variables to improve performance and troubleshooting
    • Reduce cardinality
    • Optimize DirectQuery models with table level storage
    • Create and manage aggregations
    • Check your knowledge
    • Summary

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