エピソード

  • 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.
    続きを読む 一部表示
    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.
    続きを読む 一部表示
    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.
    続きを読む 一部表示
    9 分
  • The Function That Bills by the Row

    The Function That Bills by the Row
    2026/06/19

    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.
    続きを読む 一部表示
    9 分
  • The AI That Doesn't Send an Invoice

    The AI That Doesn't Send an Invoice
    2026/06/12

    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.
    続きを読む 一部表示
    9 分
  • When Autolog Stops Logging

    When Autolog Stops Logging
    2026/06/05
    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 abstractionWhere the obvious answer breaksA real Reddit/Microsoft Q&A question unpackedThe pattern we keep seeing in the fieldRisks of the recommended pathF-SKU realism — what this actually costsWhen the rejected approach is actually rightThe concrete recommended architectureThe 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-upgradeMachine learning experiments in Microsoft FabricAutologging in Microsoft FabricWhat is Data Science in Microsoft Fabric?Analyze and train data in Microsoft FabricHyperparameter tuning in FabricPerform hyperparameter tuning with FLAMLTraining visualizations for AutoMLMLflow 3 in Fabric Data ScienceTutorial: Create, evaluate, and score a churn prediction modelTutorial Part 3: Train and register a machine learning modelTrain models with scikit-learn in Microsoft FabricMachine learning experiments and models Git integration and deployment pipelines (Preview)Data science roles and permissionsLineage 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.
    続きを読む 一部表示
    9 分
  • 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
    2026/05/29
    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 eraThe one question that reframes the architectural debateHow we got here — predecessor products and evolutionWhy the "obvious" answer is often wrongA 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 pathWhat Microsoft is shipping that changes the calculusThe 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 ExperimentNotebooksLakehouseDirect LakeCode-first AutoMLLow-code AutoMLSynapseMLActivatorMachine learning model in Microsoft FabricWhat is Data Science in Microsoft Fabric?Tutorial Part 3: Train and register a machine learning modelTutorial Part 4: Perform batch scoring and save predictionsMachine learning model scoring with PREDICTServe 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.
    続きを読む 一部表示
    10 分
  • Event Schema Set: Contracts That Stop Midnight Breakage

    Event Schema Set: Contracts That Stop Midnight Breakage
    2026/05/22
    Event Schema Set: Contracts That Stop Midnight Breakage Episode 21 • 2026-05-22 Event Schema Set is Fabric's contract layer for streaming data — but it ships in Preview with real gaps. Matthias and Fabia unpack the retrofit trap, the dead-letter gap everyone worries about, and when Confluent Schema Registry is honestly the better call. What we discuss A real-world mistake from a pre-Fabric eraThe one question that reframes the architectural debateHow we got here — predecessor products and evolutionWhy the "obvious" answer is often wrongA 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 pathWhat Microsoft is shipping that changes the calculusThe architectural principle to take home Key takeaways Treat schemas as append-only contracts. Add fields with defaults — safe. Remove required fields — breaks consumers. Change a type — silent data corruption. Rename a field — silent loss in KQL queries. The system won't stop you. Your...Fair argument. And honestly? If you're an existing Kafka shop with established Confluent practices — use Confluent. The migration cost isn't worth it. Eventstream can deserialize Confluent-encoded payloads natively. You get Avro plus JSON...But you operate a separate cluster. Separate auth. Separate billing. If your entire stack is Fabric-native — Eventstream, Notebook, Activator, Eventhouse — the integration is a real win. No client library. No external cluster. Governance... Resources Schema Registry — known limitationsCloudEvents 1.0Use schemas in eventstreamsReal-Time Hub SchemasBusiness Events ConceptsConsume Business Events from ActivatorEventhouseConfluent Kafka sourceSchema Registry in Fabric Real-Time Intelligence (preview) — OverviewCreate and manage event schema setsCreate and manage event schemas in schema setsEventSchemaSet REST API definitionEventstream Overview — Schema Management sectionMultiple-Schema Inferencing in Eventstream (Preview)Eventstream Data Formats: JSON, CSV, Avro 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.
    続きを読む 一部表示
    11 分