『Fabric Architecture Podcast』のカバーアート

Fabric Architecture Podcast

Fabric Architecture Podcast

著者： Matthias Falland
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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.© 2026 Matthias Falland 経済学
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  • The Bucket Nobody Reads

    The Bucket Nobody Reads
    2026/07/10
    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 abstractionThe pattern we keep seeing in the fieldThe concrete recommended architectureWhere the obvious answer breaksA real Reddit/Microsoft Q&A question unpackedF-SKU realism — what this actually costsWhen the rejected approach is actually rightThe 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 overviewBuild Power BI reports with Direct Lake tablesReports in Power BI - Dashboards versus reportsWhen to use paginated reportsWhat are paginated reportsIntroduction to dashboardsGit integration source code formatTour the report editorInteract with a report in Editing viewImplementation planning: user tools and devicesDirect Lake in Power BI DesktopApply data point limits and strategies by visual typeHow Direct Lake worksPower BI Desktop project report folderConnect 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.
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    11 分
  • When Direct Lake Goes Quiet

    When Direct Lake Goes Quiet
    2026/07/03

    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.
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    10 分
  • The Column Named C1

    The Column Named C1
    2026/06/26
    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 abstractionThe pattern we keep seeing in the fieldWhere the obvious answer breaksA real Reddit/Microsoft Q&A question unpackedThe concrete recommended architectureF-SKU realism — what this actually costsWhen the rejected approach is actually rightRisks of the recommended pathThe 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 conceptsdata-agent-add-datasourcesUse the Fabric data agent in Foundry — PrerequisitesConsume Fabric data agent with Python client SDKConsume Fabric data agent from Microsoft Foundry ServicesUse service principal authentication with Fabric data agentEvaluate your data agentCreate a Fabric data agentBest practices for configuring your data agentAdopt an iterative process to improve your data agentSemantic model best practices for data agentFabric data agent tenant settingsFabric data agent — Responsible AI FAQData agent configurationsWorkspace 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.
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    9 分
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