Power BI Developers/Learning

5 years ago, a good BI developer could survive by knowing Power BI really well I don’t think that’s enough anymore…. Not because Power BI is becoming less important. Because the BI ecosystem is becoming much bigger. Today, a strong BI developer should understand what happens beyond the report: 🔹 Data Engineering How data is ingested, transformed and stored. 🔹 Microsoft Fabric Lakehouse, Warehouse, Pipelines, Notebooks, Dataflows and more. 🔹 Semantic Modeling Not just creating relationships, but building a model that can actually answer business questions. 🔹 Data Governance Who owns the data? Can we trust it? Where did it come from? 🔹 AI & Copilot How do we make our data understandable and useful for AI? 🔹 Performance & Architecture Why is a report slow? Where should the transformation happen? Import, Direct Lake or DirectQuery? Power BI is still the tool but understanding the entire data journey is becoming the real skill.

Interview Highlights!! EY — Power BI Developer Technical Round (14 LPA) Panel: 2 Interviewers Duration: 55 Minutes Q1. How do you identify and fix a table scan that should be a seek? Q2. What is the difference between a heap and a clustered table in SQL Server? Q3. Explain CALCULATE with FILTER vs CALCULATE with a boolean condition directly — which is better and why? Q4. What is ALLNOBLANKROW, and when do you need it? Q5. Scenario: RLS needs to hide an entire table from one user role but show it to others. How do you do this without breaking their other reports? Q6. What is OneLake, and how is it different from a regular ADLS Gen2 container? Q7. Difference between a Fabric Lakehouse and a Fabric Warehouse — when do you pick each? Q8. Scenario: A dataset refresh succeeds but the report shows yesterday's numbers. Where do you check first? Q9. What is Direct Lake mode, and how is "framing" different from a traditional refresh? Q10. What is a bridge table, and when is it better than a direct many-to-many relationship? Q11. Explain ISFILTERED and ISCROSSFILTERED — how are they different? Q12. Scenario: A Direct Lake report suddenly starts showing "DirectQuery" in Performance Analyzer instead of Direct Lake. What caused the fallback? Q13. What causes "ambiguous relationship path" errors, and how do you resolve them? Q14. How does a Fabric capacity (F-SKU) get shared across Power BI, Lakehouse, and Data Factory workloads? Q15. How do you version-control a Power BI project (PBIP / Git integration)? Pc: Srinivasalu B V

Before Fabric — a typical enterprise data stack looked like this: ❌ Azure Data Factory → for pipelines ❌ Azure Synapse → for warehousing ❌ Azure Databricks → for data science ❌ Power BI Premium → for reporting ❌ Azure Data Lake → for storage 5 different tools. 5 different licenses. 5 different teams. With Microsoft Fabric — it's now: ✅ ONE platform ✅ ONE OneLake storage ✅ ONE license ✅ ONE unified workspace Pipelines ✅ Lakehouse ✅ Warehouse ✅ Notebooks ✅ Power BI ✅ Copilot All in one place. No data movement. No silos. The cost saving alone is making CFOs pay attention. The productivity gain is making data teams move faster than ever. Yet in most Indian IT companies today: → Teams are still running on Synapse + ADF + Power BI separately → Data engineers and BI developers work in silos → Nobody has even opened a Fabric workspace yet The companies that move to Fabric in the next 12 months will have a serious competitive advantage.

https://community.fabric.microsoft.com/t5/Power-BI-Updates-Blog/Bringing-Power-BI-Insights-to-Every-Copilot-User/ba-p/5309360?trk=feed_main-feed-card_reshare_feed-article-content

Power BI developers, your lives just got a lot easier. Say goodbye to the "Download to Desktop -> Change one minor setting -> Re-publish" cycle. Model Options are now available directly in the Power BI Service! Microsoft is heavily investing in making the web modeling experience feature-parity with Desktop. With this latest update, you can handle core engine parameters straight from your browser workspace: What's New in Web Modeling: • Auto-Type & Header Detection for raw data streams. • Relationship Configurations to manage auto-detected joins. • Parallel Table Loading to customize data processing concurrency. • Time Intelligence switches to drop or keep auto-generated date tables. • Direct query Settings to cap maximum source connections or toggle model access. Credits: Sindhu Ramasamy

Microsoft Fabric vs Databricks. I've used both in production. Here's my honest take — no vendor bias, no sponsored opinion. Where Fabric wins: If your company is already deep in the Microsoft ecosystem — Azure, Power BI, Teams, Office 365 — Fabric is a no-brainer. The integration is seamless. You don't need a separate governance layer. Your business users can already navigate it. I recently delivered a full lakehouse on Fabric in 4 weeks. The OneLake architecture, the native Power BI integration, the pipeline simplicity — it genuinely cuts time to value for mid-size enterprises. Where Databricks wins: Complex, large-scale data transformations. Multi-cloud environments. Teams with strong Python and Spark expertise. When you need Delta Lake at its most powerful, when you're running serious ML workloads, when your data volumes are massive — Databricks is still the stronger engine. It's also more mature. The Unity Catalog, the job orchestration, the notebook collaboration — it's a more complete platform right now for pure data engineering at scale. The honest answer nobody wants to hear: 𝐅𝐨𝐫 𝟖𝟎% 𝐨𝐟 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬, 𝐢𝐭 𝐝𝐨𝐞𝐬𝐧'𝐭 𝐦𝐚𝐭𝐭𝐞𝐫 𝐰𝐡𝐢𝐜𝐡 𝐨𝐧𝐞 𝐲𝐨𝐮 𝐩𝐢𝐜𝐤. What matters is whether your data is clean, your pipelines are reliable, and your business users can actually trust the numbers. I've seen beautiful Databricks implementations that nobody used. I've seen simple Fabric setups that transformed how a company makes decisions. The tool is never the problem. Credits: Mayur muttur

Credits: Akanksha Jain

https://m.youtube.com/watch?v=SStJcXn5vGE&list=PLv2BtOtLblH1IJqcqSuMTyvEi7W-laWti&index=1&pp=iAQB

https://blog.crossjoin.co.uk/2026/07/12/the-benefits-of-using-direct-lake-mode-in-power-bi/?trk=feed_main-feed-card_feed-article-content

https://www.reddit.com/r/PowerBI/comments/1ryl18t/we_benchmarked_4_ai_models_on_refactoring/

https://tabulareditor.com/blog/what-is-a-semantic-layer

https://www.linkedin.com/pulse/power-bi-shrinking-semantic-models-run-length-jonathan-toft-otykier-iiqee?trk=feed_main-feed-card_reshare_feed-article-content

https://youtu.be/wtCWuf14vUw?si=NxpjkcMjgZ_DKNGM

https://bim-slimmer.lovable.app/?trk=feed_main-feed-card_feed-article-content

https://tabulareditor.com/blog/semantic-modeling-patterns-with-power-bi-and-databricks?utm_content=397231338&utm_medium=social&utm_source=linkedin&hss_channel=lcp-72504234