Explicit Measures Podcast

A lively debate on whether Direct Lake replaces Import for Power BI and when each approach still matters. They unpack Direct Lake basics, V-ordering performance tricks, and how semantic models link to lakehouses. Licensing trade-offs and model size thresholds get scrutiny. The hosts also cover MCP’s role connecting LLMs to models and how AI could automate notebooks and refactoring.

They tackle filter overload in Power BI and debate whether many slicers help or harm report usability. They cover new Fabric features like APIs, notebooks, and authentication improvements. They explore using visuals as interactive filters, when cross-filtering suffices, and strategies like headers, filter panes, bookmarks, and prebaked views to reduce clutter.

A deep dive on why Power BI field parameters can break the Excel live-connection experience and what that means for model design. A debate about single semantic models versus thin, report-specific layers. Practical tradeoffs for serving Excel users and when to keep parameter logic out of the core model.

The conversation explores how AI and Microsoft Fabric are reshaping the traditional report developer role. They debate semantic modeling versus lakehouse approaches for ML and data science. The hosts compare paginated, pixel-perfect exports with interactive Power BI visuals and discuss when each is needed. Practical hiring lenses and day-to-day responsibilities for modern BI teams are examined.

A conversation about whether Microsoft Fabric can be trusted like Power BI and which components already feel reliable. They dig into Semantic Link becoming generally available and the strengths of notebooks, Spark, Python and Fabric SQL. The hosts compare Fabric's power and complexity to Power BI simplicity and highlight who benefits most from adopting Fabric.

Discussion on how to structure Microsoft Fabric workspaces and tradeoffs of medallion architecture versus deployment complexity. Debate over whether centralized control helps or hinders self-service at scale. Examination of how AI and agents actually increase governance needs and expand work. Practical patterns for workspace placement, access layers, and delegation strategies.

We made it 500! Get in touch:Send in your questions or topics you want us to discuss by tweeting to @PowerBITips with the hashtag #empMailbag or submit on the PowerBI.tips Podcast Page.Visit PowerBI.tips: https://powerbi.tips/Watch the episodes live every Tuesday and Thursday morning at 730am CST on YouTube: https://www.youtube.com/powerbitipsSubscribe on Spotify: https://open.spotify.com/show/230fp78XmHHRXTiYICRLVvSubscribe on Apple: https://podcasts.apple.com/us/podcast/explicit-measures-podcast/id1568944083‎Check Out Community Jam: https://jam.powerbi.tipsFollow Mike: https://www.linkedin.com/in/michaelcarlo/Follow Tommy: https://www.linkedin.com/in/tommypuglia/

Mike & Tommy explore whether being late to the data game is actually an advantage in the AI era, questioning if organizations building fresh Fabric implementations can leapfrog legacy technical debt—or if they're doomed to repeat the same governance mistakes. They tackle Sandra's mailbag question about late adoption, semantic modeling for AI readiness, and when to let ontology "brew" versus act now.Get in touch:Send in your questions or topics you want us to discuss by tweeting to @PowerBITips with the hashtag #empMailbag or submit on the PowerBI.tips Podcast Page.Visit PowerBI.tips: https://powerbi.tips/Watch the episodes live every Tuesday and Thursday morning at 730am CST on YouTube: https://www.youtube.com/powerbitipsSubscribe on Spotify: https://open.spotify.com/show/230fp78XmHHRXTiYICRLVvSubscribe on Apple: https://podcasts.apple.com/us/podcast/explicit-measures-podcast/id1568944083‎Check Out Community Jam: https://jam.powerbi.tipsFollow Mike: https://www.linkedin.com/in/michaelcarlo/Follow Tommy: https://www.linkedin.com/in/tommypuglia/

Hosts warn against buying tools before defining the decision you want to improve. They debate how AI and Fabric change work, discovery, and metadata needs. Practical ideas include AI-assisted data discovery, reusable model views, and breaking problems into tasks agents can run. Discussion also covers governance, privacy, and where personalization fits in modern analytics.

They dig into connecting local VS Code to remote Fabric Jupyter kernels and whether Fabric fits traditional data science workflows. They compare remote kernels, VMs, and local development trade offs. They discuss custom Fabric environments, data movement with OneLake, and how to make notebooks portable between local and remote runs.