Finale

Namaste Power BI

Episode 43

IPL Project: Data Engineering & Schema

⏱ 50:00

Episode Summary

Finale Episode 1 — designing the IPL analytics data model. Ball-by-ball fact table, Player/Team/Match/Venue dimensions, surrogate keys, and building a cricket analytics star schema from raw data.

This is Episode 43 of the free Namaste Power BI course on DevWithData — covering ipl project: data engineering & schema. Watch the video above, then pass the MCQ checkpoint to unlock the next episode and update your Data Readiness Index score.

Key Takeaways

→Ball-by-ball is the lowest grain — every delivery is a row in the fact table
→Player dimension includes both batting and bowling attributes
→Match dimension links to Venue, Season, and Team dimensions
→Venue dimension enables geography-based analytics (home vs away performance)

⚠ Common Interview Pitfalls

•Using match-level grain (too coarse for ball-by-ball analysis)
•Not handling player roles correctly (a player can bat AND bowl)
•Forgetting to create a proper Season dimension for trend analysis

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 42: Career Scaling & What's Next

Ep 44: IPL Project: Advanced DAX Analytics →

Episode Checkpoint

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