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Normalized vs Denormalized Models in Power BI: Why Star Schemas Perform Better

Lesson 16

Normalized vs Denormalized Models in Power BI: Why Star Schemas Perform Better

Normalized models are usually a poor fit for Microsoft Power BI because they create complex joins, slower queries, ambiguous relationships, and harder-to-maintain reports. To denormalize a snowflaked source, teams typically flatten related dimension tables into a simpler star schema structure by merging lookup tables, reducing join depth, and creating clean fact and dimension tables optimized for analytics and DAX performance.

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Curriculum

21 lessons · 43m

0/21 lessons done43m left
  1. 0101:59

    What Is Power BI and What Business Problem Does It Solve?

    01:59

  2. 0202:05

    Main Components of Power BI Explained Simply

    02:05

  3. 0302:03

    Power BI Desktop vs Service vs Mobile: What’s the Difference?

    02:03

  4. 0401:53

    Power BI Licensing Explained: Free vs Pro vs PPU vs Fabric F-SKU

    01:53

  5. 0502:11

    PBIX vs PBIT in Power BI: What’s the Difference?

    02:11

  6. 0602:07

    Power BI Workflow: From Data Connection to Report Publishing

    02:07

  7. 0702:06

    Power BI in Microsoft Fabric: Semantic Models and the New Unified Analytics Architecture

    02:06

  8. 0801:59

    Power BI Semantic Model vs Report vs Dashboard: What’s the Difference?

    01:59

  9. 0902:01

    Fact Table vs Dimension Table in Data Modeling Explained

    02:01

  10. 1002:12

    Star Schema in Power BI: Why It’s the Best Data Modeling Pattern

    02:12

  11. 1102:06

    Star Schema vs Snowflake Schema: What’s the Difference in Data Modeling?

    02:06

  12. 1201:57

    Power BI Relationship Cardinality: One-to-Many vs Many-to-Many Explained

    01:57

  13. 1302:00

    Single-Direction vs Bi-Directional Cross-Filtering in Power BI: When Should You Use Each?

    02:00

  14. 1401:58

    Active vs Inactive Relationships in Power BI: How to Use Inactive Relationships in Measures

    01:58

  15. 1501:53

    Role-Playing Dimensions in Power BI: How to Model Multiple Date Relationships Efficiently

    01:53

  16. 02:15

    Normalized vs Denormalized Models in Power BI: Why Star Schemas Perform Better

    02:15

  17. 1702:19

    Power Query and M Language in Power BI: What Are They Used For?

    02:19

  18. 1802:05

    Power Query vs DAX in Power BI: When Should You Use Each?

    02:05

  19. 1902:00

    What is query folding, and why is it important for performance?

    02:00

  20. 2001:47

    What is the difference between Merge Queries and Append Queries

    01:47

  21. 2102:03

    What is a Power Query parameter, and give one real use case for it.

    02:03