Fabric Architecture Podcast

Matthias Falland

Architecture decisions for Microsoft Fabric. Anonymized real customer scenarios, cost realism, counter-arguments included. Weekly episodes aligned with Fabric Friday recordings.

  1. vor 5 Tagen

    The Dashboard That Can't Filter

    The Dashboard That Can't Filter Episode 29 • 2026-07-17 Duration: 9:51 A Power BI dashboard can show tiles from six different semantic models. It cannot filter a single one of them. We explain why the architecture makes that tradeoff inevitable, and what it means for choosing between dashboards and reports. What we discuss How it actually works underneath the abstraction The pattern we keep seeing in the field A real Reddit/Microsoft Q&A question unpacked Where the obvious answer breaks F-SKU realism — what this actually costs When the rejected approach is actually right Risks of the recommended path The concrete recommended architecture The architectural principle to take home Key takeaways Both problems solve the moment you understand what you pinned. Somewhere right now, someone's pinning a tile and wondering where the slicer went. Show me the query pattern. If the pattern is 'monitor five numbers from five models,' the dashboard's exactly right. If the pattern is 'explore, filter, drill,' you wanted a report from the start. Resources Introduction to dashboards for Power BI designers Compare Report Server and the Power BI service Introduction to dashboard tiles Pin Reporting Services items to Power BI dashboards Pin an entire report page as a live tile Reports in Power BI Create a Real-Time Dashboard What is Fabric Embed (preview) Conduct proof of concept to migrate to Power BI mobile-apps-view-dashboard service-dashboard-pin-tile-from-excel service-pin-tile-to-another-dashboard Dashboards for business users Create a Power BI dashboard from a report, Considerations and limitations Create or edit a dashboard tile About the show AI-generated voices. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon. Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday. Submit your case Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing. This podcast was generated by AI. Brand design based on fabricperiodictable.com.

    The Dashboard That Can't Filter

  2. 10. Juli

    The Bucket Nobody Reads

    The Bucket Nobody Reads Episode 28 • 2026-07-10 Duration: 10:31 Every visual on a report page fires a DAX query. When twenty visuals queue behind a parallelism cap, the bottleneck hides in Performance Analyzer's least-read column — and the escape hatch everyone reaches for can silently produce wrong numbers. What we discuss How it actually works underneath the abstraction The pattern we keep seeing in the field The concrete recommended architecture Where the obvious answer breaks A real Reddit/Microsoft Q&A question unpacked F-SKU realism — what this actually costs When the rejected approach is actually right The architectural principle to take home Key takeaways Somewhere right now, a report page is loading twenty visuals, queueing twelve of them behind a parallelism cap, and the person watching the spinner is about to open Performance Analyzer, see a DAX number, and start tuning the wrong thing. Show me the query pattern. That's what this comes down to. A report is a query generator. Every visual is a query, every page load is a fan-out, and every performance problem lives in the gap between what the author designed and what the... The piece I'd take away is that the report author and the model owner have to be in the same conversation. Resources Power BI reports overview Build Power BI reports with Direct Lake tables Reports in Power BI - Dashboards versus reports When to use paginated reports What are paginated reports Introduction to dashboards Git integration source code format Tour the report editor Interact with a report in Editing view Implementation planning: user tools and devices Direct Lake in Power BI Desktop Apply data point limits and strategies by visual type How Direct Lake works Power BI Desktop project report folder Connect to semantic models from Power BI Desktop About the show AI-generated voices. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon. Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday. Submit your case Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing. This podcast was generated by AI. Brand design based on fabricperiodictable.com.

    The Bucket Nobody Reads

  3. 3. Juli

    When Direct Lake Goes Quiet

    When Direct Lake Goes Quiet Episode 27 • 2026-07-03 Duration: 9:40 Direct Lake promises no refresh and VertiPaq speed at lake scale. But its failure modes are silent — it degrades instead of breaking. We pull apart framing, transcoding, and the fallback tax nobody talks about. What we discuss How it actually works underneath the abstraction A real Reddit/Microsoft Q&A question unpacked Where the obvious answer breaks The concrete recommended architecture The pattern we keep seeing in the field F-SKU realism — what this actually costs When the rejected approach is actually right Risks of the recommended path The architectural principle to take home Key takeaways Somewhere right now, a semantic model is serving yesterday's numbers. Run TABLETRAITS before you ship. One line of DAX. It tells you whether your model is actually in Direct Lake mode — or quietly serving something else. So the lesson — Direct Lake moved where the discipline lives. Resources Power BI semantic models in Microsoft Fabric Semantic models in the Power BI service Store data in Microsoft Fabric Direct Lake overview Semantic model modes New name for Power BI datasets Develop Direct Lake semantic models Direct Lake in web modeling How Direct Lake works Understand Direct Lake query performance Analyze query processing for Direct Lake semantic models Cross-workload table maintenance and optimization Optimize Delta Lake tables with V-Order Dimensional modeling in Fabric Warehouse IDEAS journey to a modern data platform About the show AI-generated voices. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon. Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday. Submit your case Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing. This podcast was generated by AI. Brand design based on fabricperiodictable.com.

    When Direct Lake Goes Quiet

  4. 26. Juni

    The Column Named C1

    The Column Named C1 Episode 26 • 2026-06-26 Duration: 9:21 A Fabric Data Agent generates queries from your column names, not your intentions. This episode pulls apart the grounding pipeline to show where accuracy lives and dies — and why the schema investment you skipped three years ago just became urgent. What we discuss How it actually works underneath the abstraction The pattern we keep seeing in the field Where the obvious answer breaks A real Reddit/Microsoft Q&A question unpacked The concrete recommended architecture F-SKU realism — what this actually costs When the rejected approach is actually right Risks of the recommended path The architectural principle to take home Key takeaways Somewhere, a data architect who's been filing those tickets for five years just felt a wave of vindication. There's something to that. We spent years telling teams to name their columns properly and nobody listened, because the SQL worked either way. Now there's an LLM reading those column names and getting the wrong answer, and suddenly the... Show me the query pattern — then show me the auth pattern. Resources Fabric data agent concepts data-agent-add-datasources Use the Fabric data agent in Foundry — Prerequisites Consume Fabric data agent with Python client SDK Consume Fabric data agent from Microsoft Foundry Services Use service principal authentication with Fabric data agent Evaluate your data agent Create a Fabric data agent Best practices for configuring your data agent Adopt an iterative process to improve your data agent Semantic model best practices for data agent Fabric data agent tenant settings Fabric data agent — Responsible AI FAQ Data agent configurations Workspace outbound access protection for Data Agent (Preview) About the show AI-generated voices. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon. Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday. Submit your case Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing. This podcast was generated by AI. Brand design based on fabricperiodictable.com.

    The Column Named C1

  5. 19. Juni

    The Function That Bills by the Row

    The Function That Bills by the Row Episode 25 • 2026-06-19 Duration: 9:01 AI Functions let you call GPT from a SELECT statement with no API key. Zero infrastructure — until you check the capacity meter. Two billing streams, no result cache, and a default model eleven days from retirement. What we discuss How it actually works underneath the abstraction Where the obvious answer breaks A real Reddit/Microsoft Q&A question unpacked The pattern we keep seeing in the field F-SKU realism — what this actually costs When the rejected approach is actually right The concrete recommended architecture Risks of the recommended path The architectural principle to take home Key takeaways Ticking right now, in every tenant where someone put ai_classify in a view and moved on. That quiet meter in your Capacity Metrics app. Pattern dictates platform. If your pattern is "enrich once, read many," AI Functions are the shortest path in Fabric right now. If that pattern drifts into "enrich on every read," you're billing the LLM like a column default. Resources AI Functions overview Use Azure OpenAI in Fabric with AI Functions (preview) Warehouse AI functions (preview) pandas similarity docs PySpark similarity docs pandas translate docs Foundry Tools consumption rate page Notebooks in Fabric Fabric Data Warehouse Apache Spark in Fabric Copilot in Fabric Data Agent in Fabric Lakehouse overview Customize AI Functions with pandas Customize AI Functions with PySpark About the show AI-generated voices. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon. Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday. Submit your case Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing. This podcast was generated by AI. Brand design based on fabricperiodictable.com.

    The Function That Bills by the Row

  6. 12. Juni

    The AI That Doesn't Send an Invoice

    The AI That Doesn't Send an Invoice Episode 24 • 2026-06-12 Duration: 9:11 Copilot in Fabric has no per-user license fee. But every request draws from the same capacity pool as your pipelines and reports. We dig into what 'included' actually costs — and when it throttles production. What we discuss How it actually works underneath the abstraction The pattern we keep seeing in the field A real Reddit/Microsoft Q&A question unpacked F-SKU realism — what this actually costs The concrete recommended architecture When the rejected approach is actually right Where the obvious answer breaks Risks of the recommended path The architectural principle to take home Key takeaways Somewhere right now, there's an F2 running Copilot for a whole team. The most expensive support ticket you'll ever file is the one where the answer is "rename your columns. Pattern dictates platform. And with Copilot, the pattern is this — it's a productivity tool that's already wired into your cost structure whether you planned for it or not. The teams that get value from it are the ones who treat enablement... Resources copilot-privacy-security copilot-fabric-overview data-warehouse/copilot copilot-enable-power-bi known-limitations table Copilot in Fabric consumption how-copilot-works Copilot and Agent tenant settings copilot-notebooks-overview Enable Fabric Copilot for Power BI SQL DB Copilot FAQ overview docs How Copilot in Microsoft Fabric works Enable Copilot in Fabric Fabric Copilot capacity About the show AI-generated voices. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon. Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday. Submit your case Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing. This podcast was generated by AI. Brand design based on fabricperiodictable.com.

    The AI That Doesn't Send an Invoice

  7. 5. Juni

    When Autolog Stops Logging

    When Autolog Stops Logging Episode 23 • 2026-06-05 Duration: 8:47 Fabric's MLflow autologging promises zero-effort experiment tracking — until you train with LightGBM or XGBoost and discover your metrics column is blank. We pull apart the framework gap, the compounding exclusive flag, and the Git backup that isn't one. What we discuss How it actually works underneath the abstraction Where the obvious answer breaks A real Reddit/Microsoft Q&A question unpacked The pattern we keep seeing in the field Risks of the recommended path F-SKU realism — what this actually costs When the rejected approach is actually right The concrete recommended architecture The architectural principle to take home Key takeaways Somewhere right now, a training run just finished. And name your runs. Pass run_name to start_run. Forty runs all called Run followed by a number, and the comparison pane becomes useless regardless of how clean the rest of your tracking is. The takeaway I'd leave anyone starting with Fabric Experiments — the abstraction is real, but it has seams you need to know on day one. Resources mlflow-upgrade Machine learning experiments in Microsoft Fabric Autologging in Microsoft Fabric What is Data Science in Microsoft Fabric? Analyze and train data in Microsoft Fabric Hyperparameter tuning in Fabric Perform hyperparameter tuning with FLAML Training visualizations for AutoML MLflow 3 in Fabric Data Science Tutorial: Create, evaluate, and score a churn prediction model Tutorial Part 3: Train and register a machine learning model Train models with scikit-learn in Microsoft Fabric Machine learning experiments and models Git integration and deployment pipelines (Preview) Data science roles and permissions Lineage for models and experiments About the show AI-generated voices. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon. Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday. Submit your case Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing. This podcast was generated by AI. Brand design based on fabricperiodictable.com.

    When Autolog Stops Logging

  8. 29. Mai

    ML Models in Fabric: Training, Deployment, and When to Stay on Azure ML

    ML Models in Fabric: Training, Deployment, and When to Stay on Azure ML Episode 22 • 2026-05-29 Microsoft Fabric ships its own MLflow registry — but is it a replacement for Azure Machine Learning? Matthias and Fabia work through the four-layer registry model, PREDICT versus Model Endpoints, the Direct Lake prediction loop, and the architectural question that actually determines the answer: where do your predictions land? What we discuss A real-world mistake from a pre-Fabric era The one question that reframes the architectural debate How we got here — predecessor products and evolution Why the "obvious" answer is often wrong A real Reddit/Microsoft Q&A question unpacked The concrete recommended architecture F-SKU realism — what this actually costs When the rejected approach is actually right Risks of the recommended path What Microsoft is shipping that changes the calculus The architectural principle to take home Key takeaways Where do the predictions land. That question answers the architecture. OneLake plus Power BI Direct Lake — Fabric ML Model, genuinely the right call. REST API for an app — evaluate Endpoints maturity or route to Azure ML. GPU training,... I'd go further. Already on Databricks with Unity Catalog? Don't migrate. Fabric ML Model is not a migration target for Databricks shops — the platform maturity gap is real. The hybrid that actually works: train on Azure ML with GPU,... For Power BI shops — yes. PREDICT writes predictions to a Delta table in OneLake, Direct Lake reads it with zero copy, zero scheduled refresh. That eliminates an entire class of ETL work. But only if Power BI is your audience. Resources ML Experiment Notebooks Lakehouse Direct Lake Code-first AutoML Low-code AutoML SynapseML Activator Machine learning model in Microsoft Fabric What is Data Science in Microsoft Fabric? Tutorial Part 3: Train and register a machine learning model Tutorial Part 4: Perform batch scoring and save predictions Machine learning model scoring with PREDICT Serve real-time predictions with ML model endpoints (Preview) Train models with scikit-learn in Microsoft Fabric About the show Built on ElevenLabs voice synthesis. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon. Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday. Submit your case Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing. Built on ElevenLabs voice synthesis. Brand design based on fabricperiodictable.com.

    ML Models in Fabric: Training, Deployment, and When to Stay on Azure ML
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Architecture decisions for Microsoft Fabric. Anonymized real customer scenarios, cost realism, counter-arguments included. Weekly episodes aligned with Fabric Friday recordings.

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  • Erstellt von
    Matthias Falland
  • Jahre aktiv
    2026
  • Folgen
    30
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    © 2026 Matthias Falland
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    Fabric Architecture Podcast