Season 3

Namaste Power BI

Episode 36

Dataflows

⏱ 35:00

Episode Summary

Building reusable, cloud-based ETL pipelines — Power BI Dataflows for centralized data preparation, shared transformation logic across multiple datasets, and reducing redundant Power Query work.

This is Episode 36 of the free Namaste Power BI course on DevWithData — covering dataflows. Watch the video above, then pass the MCQ checkpoint to unlock the next episode and update your Data Readiness Index score.

Key Takeaways

→Dataflows run Power Query in the cloud — no Desktop needed
→Shared logic: one dataflow serves multiple datasets (single source of truth)
→Standard dataflows store data in CDM format in built-in storage
→Analytical dataflows (Gen2) use Azure Data Lake Storage for advanced scenarios

⚠ Common Interview Pitfalls

•Creating separate Power Query logic in every .pbix file (use Dataflows)
•Not scheduling dataflow refresh before dependent dataset refresh
•Overcomplicating dataflows when simple Power Query in Desktop suffices

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 36: Incremental Refresh

Ep 37: Microsoft Fabric & Licensing →

Episode Checkpoint

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