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Microsoft

Model data with Power BI

Microsoft via Microsoft Learn

Overview

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  • Module 1: In this module, you'll learn about the Power BI Desktop model structure, star schema design basics, analytics queries, and report visual configuration. This module provides a strong foundation on which you can learn to optimize model designs and add model calculations.

    By the end of this module, you'll be able to:

    • Describe the structure of a Power BI Desktop model.
    • Explain star schema design basics.
    • Define the term analytic query and its phases.
    • Describe how fields can be used to configure a report visual, which then generates an analytic query.
  • Module 2: 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 3: 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 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 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 4: In this module, you'll learn how to write DAX formulas to create calculated tables, calculated columns, and measures, which are different types of model calculations. Additionally, you'll learn how to write and format DAX formulas, which consist of expressions that use functions, operators, references to model objects, constants, and variables.

    By the end of this module, you'll be able to:

    • Describe the different DAX calculation types.
    • Write DAX formulas.
    • Describe DAX data types.
    • Work with DAX functions.
    • Use DAX operators.
    • Use DAX variables.
  • Module 5: In this module, you'll learn how to work with implicit and explicit measures. You'll start by creating simple measures, which summarize a single column or table. Then, you'll create more complex measures based on other measures in the model. Additionally, you'll learn about the similarities of, and differences between, a calculated column and a measure.

    By the end of this module, you'll be able to:

    • Determine when to use implicit and explicit measures.
    • Create simple measures.
    • Create compound measures.
    • Create quick measures.
    • Describe similarities of, and differences between, a calculated column and a measure.
  • Module 6: By the end of this module, you'll be able to add calculated tables and calculated columns to your data model. You'll also be able to describe row context, which is used to evaluated calculated column formulas. Because it's possible to add columns to a table by using Power Query, you'll also learn when it's best to create calculated columns instead of Power Query custom columns.

    By the end of this module, you'll be able to:

    • Create calculated tables.
    • Create calculated columns.
    • Identify row context.
    • Determine when to use a calculated column in place of a Power Query custom column.
    • Add a date table to your model by using DAX calculations.
  • Module 7: By the end of this module, you'll learn the meaning of time intelligence and how to add time intelligence DAX calculations to your model. These calculations will include year-to-date (YTD), year-over-year (YoY) growth, and others.

    By the end of this module, you'll be able to:

    • Define time intelligence.
    • Use common DAX time intelligence functions.
    • Create useful intelligence calculations.
  • Module 8: 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.
  • Module 9: Enforce model security in Power BI using row-level security and object-level security.

    By the end of this module, you’ll be able to:

    • Restrict access to Power BI model data with RLS.
    • Restrict access to Power BI model objects with OLS.
    • Apply good development practices to enforce Power BI model security.

Syllabus

  • Module 1: Module 1: Describe Power BI Desktop models
    • Introduction
    • Star schema design
    • Analytic queries
    • Configure report visuals
    • Check your knowledge
    • Summary
  • Module 2: Module 2: 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 3: Module 3: 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
    • Exercise - Model data in Power BI Desktop
    • Check your knowledge
    • Summary
  • Module 4: Module 4: Write DAX formulas for Power BI Desktop models
    • Introduction
    • Write DAX formulas
    • DAX data types
    • Work with DAX functions
    • Use DAX operators
    • Use DAX variables
    • Check your knowledge
    • Summary
  • Module 5: Module 5: Add measures to Power BI Desktop models
    • Introduction
    • Create simple measures
    • Create compound measures
    • Create quick measures
    • Compare calculated columns with measures
    • Check your knowledge
    • Exercise - Create DAX Calculations in Power BI Desktop
    • Summary
  • Module 6: Module 6: Add calculated tables and columns to Power BI Desktop models
    • Introduction
    • Create calculated columns
    • Learn about row context
    • Choose a technique to add a column
    • Check your knowledge
    • Summary
  • Module 7: Module 7: Use DAX time intelligence functions in Power BI Desktop models
    • Introduction
    • Use DAX time intelligence functions
    • Additional time intelligence calculations
    • Exercise - Create Advanced DAX Calculations in Power BI Desktop
    • Check your knowledge
    • Summary
  • Module 8: Module 8: 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
  • Module 9: Module 9: Enforce Power BI model security
    • Introduction
    • Restrict access to Power BI model data
    • Restrict access to Power BI model objects
    • Apply good modeling practices
    • Exercise: Enforce model security
    • Knowledge check
    • Summary

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