Data modeling is a required skill to get the best out of Power BI, Power Pivot for Excel, and Analysis Services. This video course is aimed at users of Power BI Desktop or Power Pivot for Excel, and at Analysis Services developers who want to learn how to build the optimal data model for their reporting needs.

The goal of the course is to teach through examples of increasing complexity how to solve business scenarios by adapting the data model, so that the required DAX code becomes easier, faster and more robust. All the demos and the exercises are based on Power BI examples. However, the very same concepts can be applied to Power Pivot and Analysis Services Tabular.

The course is made of more than 10 hours of lectures, and other 8 hours of individual exercises. You can watch the videos at any time and the system will keep track of your advances. Within the course you can download the material for all the exercises.


  • Presentation of Data Modeling for Power BI

    • Presentation of Data Modeling for Power BI
  • Exercises, slides, and demos

    • How to download and complete exercises
    • Exercises download
    • Demos download
    • Slides of the video course
    • SQL Server Contoso DW database
  • Introduction to data modeling

    • Introduction to data modeling
    • Introduction
    • Analytical limits
    • Increasing the analytical power
    • Introducing the data model
    • Leveraging the data model
    • Normalization and denormalization
    • Facts and dimensions
    • Introducing star schemas
    • If you don't have a star schema
    • Chains of relationships
    • How many dimensions?
    • Why data modeling is useful
    • LAB number 1
  • Header / detail tables

    • Header / detail tables
    • Introducing header / detail schemas
    • Bidirectional filtering is not the way to go
    • Header / detail
    • Denormalizing the discount
    • Back to a star schema
    • LAB number 2
  • Multiple fact tables

    • Multiple fact tables
    • Using multiple fact tables
    • Moving filters with DAX
    • Building a star schema
    • How to properly use multiple fact tables, if present
    • Trying bidirectional filtering
    • Model ambiguity
    • Solving ambiguity
    • LAB number 3
  • Working with date and time

    • Working with date and time
    • Date attributes in the fact table
    • Building a date dimension
    • The model with a date dimension
    • Automatic date grouping in Excel and Power BI
    • Automatic date grouping in Power BI
    • Quick calculations in Power BI Desktop
    • Disable automatic date columns
    • Handling multiple dates
    • Multiple date tables
    • Multiple date tables with multiple fact tables
    • Multiple relationships with date
    • Handling date and time
    • Computing with working days
    • Create a holidays table (one country)
    • Create a holidays table (multiple countries)
    • Weekends are not always the same
    • Handling special periods of the year
    • Non-overlapping periods: the model
    • Non-overlapping special periods
    • Overlapping special periods
    • Overlapping period measure
    • LAB number 4
  • Tracking historical attributes

    • Tracking historical attributes
    • Attributes change over time
    • Handling variations over time
    • Slowly changing attributes or dimensions?
    • Rapidly changing dimensions
    • Attributes in the fact table
  • Using snapshots

    • Using snapshots
    • What is a snapshot?
    • Sales versus inventory in the same model
    • Non-additive measures
    • LASTDATE does not work here
    • Optimizing performance
    • Snapshots and granularity
    • Transition matrix
    • Parameter table
    • LAB number 5
  • Analyzing date and time intervals

    • Analyzing date and time intervals
    • What are intervals?
    • Solving with DAX... too complex!
    • Changing granularity
    • Split hours AND amount!
    • Analyzing active events
    • Open orders: the starting model
    • Open orders with DAX
    • Changing the model
    • Open orders is a snapshot table
    • Events with different durations
    • Daily Salary (DAX)
    • Precompute the values
    • LAB number 6
  • Many-to-many relationships

    • Many-to-many relationships
    • What are many-to-many relationships?
    • Possible solutions to the scenario
    • Bidirectional filtering
    • Using expanded table filtering
    • CROSSFILTER versus expanded tables
    • Understanding non-additivity
    • Cascading many-to-many
    • LAB number 7
  • Working with different granularities

    • Working with different granularities
    • Dimensions define granularity
    • Analyzing budget data
    • Reduce granularity on all the tables
    • Using DAX to move the filters
    • Filtering through relationships
    • Use the correct column to slice
    • Leveraging relationships
    • Checking granularity in the report
    • Hiding or reallocating?
    • LAB number 8
  • Segmentation data models

    • Segmentation data models
    • Segmentation models
    • Static segmentation
    • Static segmentation: the formula
    • Dynamic segmentation
    • Dynamic segmentation: the formula
    • ABC and Pareto analysis
  • Working with multiple currencies

    • Working with multiple currencies
    • Using multiple currencies
    • Beware of simple calculations
    • Multiple sources, one reporting currency
    • Conversion with a calculated column
    • Single source, multiple reporting currencies
    • What the formula should perform
    • Multiple sources, multiple reporting currencies

Do you prefer a course in classroom?

This video course is based on a live, classroom course we teach all around the world. If you prefer a live learning experience, take a look at the dates below for a list of our upcoming events!
 Amsterdam, NL Jun 26-28, 2018
 Sydney, AU Aug 7-9, 2018