Season 1

Namaste Power BI

Episode 7

Modeling Principles

⏱ 40:00

Episode Summary

Data modeling theory applied to Power BI — normalization vs denormalization, fact vs dimension tables, and why modeling is the #1 skill that separates amateurs from professionals.

This is Episode 7 of the free Namaste Power BI course on DevWithData — covering modeling principles. Watch the video above, then pass the MCQ checkpoint to unlock the next episode and update your Data Readiness Index score.

Key Takeaways

→Good modeling = fast DAX + correct results
→Fact tables store events/transactions (numeric, high volume)
→Dimension tables store descriptive attributes (text, low volume)
→The grain of a fact table determines everything downstream

⚠ Common Interview Pitfalls

•Putting everything in one flat table (the "mega table" anti-pattern)
•Confusing the grain — mixing daily and monthly data in the same fact
•Ignoring modeling and jumping straight to DAX measures

Official Reference

Deepen your understanding with official Microsoft documentation.

PL-300 Official Training Course

Microsoft's official instructor-led training for the Power BI Data Analyst certification.

https://learn.microsoft.com/en-us/training/courses/pl-300t00

Power BI Documentation

Complete reference for Power BI Desktop, Power BI Service, and Power BI Embedded.

https://learn.microsoft.com/en-us/power-bi/

Content on DevWithData is original and inspired by official Microsoft resources. The links above point directly to Microsoft Learn — always the authoritative source.

← Ep 6: Advanced Power Query

Ep 8: Star Schema Mastery →

Episode Checkpoint

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