Episode 42 • 2026-10-23 Duration: 12:43

メダリオンはFabricの推奨設計手法です。しかし5つある構成要素のうちの1つにすぎず、しかもアーキテクチャ図には載らないメンテナンス請求書がついてきます。Direct LakeのOneLakeにおけるファイル数ガードレールの意味、スキーマ対応Lakehouseで自動メンテナンスアクティビティが使えない理由、そしてこのパターン自体が過剰になる場面を解説します。

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
• F-SKU realism — what this actually costs
• When the rejected approach is actually right
• The concrete recommended architecture
• The architectural principle to take home

From the video

• Architecture film: Five building blocks
• Architecture film: Each layer is a contract
• Demo 1: Bronze That Copies Nothing
• Demo 2: Silver as a Contract
• Architecture film: When recipe is wrong

Resources

• Understand medallion architecture for Fabric with OneLake
• OneLake patterns and foundational capabilities
• Common dbt job patterns
• Modern data warehouse medallion architecture in Microsoft Fabric
• What are materialized lake views?
• Spark SQL reference
• Optimal refresh
• Cross-workload table maintenance and optimization
• What's new
• Lakehouse Maintenance activity
• Apache Spark billing and utilization
• extended capabilities
• CDF page
• Data quality in materialized lake views
• Manage materialized lake views lineage

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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Episode 29 • 2026-07-17 Duration: 12:37

Power BIのダッシュボードは6つの異なるセマンティック モデルからタイルを表示できます。しかし、その1つもフィルターできません。なぜアーキテクチャがそのトレードオフを不可避にしているのか、ダッシュボードとレポートの使い分けにどう影響するのかを解説します。

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

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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Episode 41 • 2026-09-18 Duration: 10:48

DatabricksとFabricの統合経路は1つではなく5つ。データは動く。権限は動かない。1つの資格情報モデル、取り消しに1時間かかる遅延、そしてUnity Catalogの行レベルのセキュリティを迂回するADLS shortcutを掘り下げます。

What we discuss

• How it actually works underneath the abstraction
• Where the obvious answer breaks
• The pattern we keep seeing in the field
• A real Reddit/Microsoft Q&A question unpacked
• 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

Watch the episode

The Fabric Friday video on this topic: https://youtu.be/RAqhXwSj_HE

From the video

• 13:33 Demo: Direct Lake Over Databricks Data
• 3:17 Demo: Mirror a Catalog from Unity Catalog
• 8:37 Demo: One Credential for Everyone, and a Revoke That Takes an Hour
• 23:49 Who Pays
• 17:02 The Reverse Direction Is Three Different Things

Resources

• Mirroring Azure Databricks Unity Catalog
• Secure Fabric mirrored databases from Azure Databricks
• Tutorial: Configure Microsoft Fabric mirrored databases from Azure Databricks
• Enable external data access to Unity Catalog
• Publish a Unity Catalog catalog to Microsoft Fabric
• Limitations in Microsoft Fabric mirrored databases from Azure Databricks
• Enable OneLake catalog federation
• Integrate OneLake with Azure Databricks
• Connect Azure Databricks to a OneLake external location
• Fabric capacity and OneLake consumption
• OneLake compute and storage consumption
• Understand your Azure bill for a Fabric capacity
• Shortcuts in a lakehouse
• Mirrored Azure Databricks Unity Catalog definition
• OneLake storage tiers

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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Episode 28 • 2026-07-10 Duration: 13:01

ビジュアル20個で20本のDAXクエリが走る。並列処理の上限を超えた分は順番待ちになるのに、ボトルネックはPerformance Analyzerで誰も見ない項目に隠れている。そして、全行をPDFに出そうとする回避策は、静かに間違った数字を返す。

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

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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Episode 27 • 2026-07-03 Duration: 12:39

Direct Lakeは更新不要でVertiPaqの速度をデータレイク規模で実現すると謳います。しかしその障害モードは沈黙です。壊れるのではなく、静かに劣化します。フレーミング、トランスコーディング、そして誰も語らないフォールバックのコストを分解します。

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

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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Episode 40 • 2026-09-25 Duration: 11:03

Cosmos DBにスキーマはない。Deltaテーブルには必要。ミラーリングが両者をつなぐとき、レプリケーターは推測する。その推測は広がる一方。バイトの出どころ、プロパティのリネームが残すゴースト列、レプリケーション停止の本当の意味を追う。

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
• 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

Watch the episode

The Fabric Friday video on this topic: https://youtu.be/KJUBmoNFICo

From the video

• 13:19 The Discovered Schema
• 16:20 Nested Data Arrives as a String
• 18:47 Stop Is Not Pause
• 2:53 Demo: Create the Mirror, Count the Rows

Resources

• Mirroring Azure Cosmos DB
• Cosmos DB mirroring FAQ
• continuous backup
• Cosmos DB mirroring limitations
• Migrate an Azure Cosmos DB account from periodic to continuous backup mode
• Migrate from Azure Synapse Link to Cosmos DB mirroring
• Access mirrored data in Lakehouse and notebooks
• How to: Query nested data
• Configure private networks for Azure Cosmos DB Fabric Mirroring
• tutorial
• Monitor Fabric mirrored database replication
• Mirroring public REST API
• Cost of mirroring
• Mirrored database in Fabric (all sources)
• Cosmos DB in Microsoft Fabric (native item)

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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Episode 26 • 2026-06-26 Duration: 11:32

Fabric Data Agentが読んでいるのは、あなたの意図ではなく列名です。このエピソードでは、精度を左右するグラウンディングパイプラインの内部構造を解剖し、3年前に後回しにしたスキーマ整備がなぜ今、最優先になったのかを掘り下げます。

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

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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Episode 25 • 2026-06-19 Duration: 11:11

AI関数なら、APIキーなしでSELECT文からGPTを呼び出せる。インフラ構築ゼロ。ただし容量メーターを確認するまでの話。課金経路は2本、結果キャッシュはなし、既定モデルの提供終了まであと11日。

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

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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Episode 38 • 2026-10-09 Duration: 10:26

Fabricのドメインはガバナンスの境界に見えますが、ドキュメントには別のことが書かれています。ドメインが実際に提供するもの、テナントで今も動き続けている既定ドメインの仕組み、そしてラベルと鍵の違いを整理します。

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 architectural principle to take home

Watch the episode

The Fabric Friday video on this topic: https://youtu.be/8wRSvnF_CtQ

From the video

• 7:46 Demo: The Contributor Who Cannot Contribute
• 3:15 Demo: The Filter That Is Not a Fence
• 12:51 Demo: An Assignment Nobody Made
• 14:42 The Sensitivity Label Override Ladder

Resources

• Fabric domains
• Power BI implementation planning: Tenant administration
• Best practices for domains
• Domain-level default sensitivity labels
• Endorsement overview
• OneLake catalog overview
• Scope the catalog to a particular domain
• Governance overview and guidance
• Roles in workspaces
• About tenant settings

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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Episode 24 • 2026-06-12 Duration: 11:41

Copilot in Fabricにはユーザーごとのライセンス料がありません。しかしリクエストのたびに、パイプラインやレポートと同じ容量プールから消費されます。「含まれている」が実際に何を意味するのか、そしていつ本番環境がスロットリングされるのかを掘り下げます。

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

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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Episode 37 • 2026-10-16 Duration: 11:14

Fabricは処理の実行時に課金するのではなく、2880個の30秒タイムポイントにコストを前方分散します。このひと言が、容量にまつわるあらゆる不可解な挙動を説明します。サージ保護が、守るべきSQLを拒否する理由も含めて。

What we discuss

• How it actually works underneath the abstraction
• 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
• Risks of the recommended path
• The architectural principle to take home

Watch the episode

The Fabric Friday video on this topic: https://youtu.be/iE-jdcseCaY

From the video

• 7:42 Demo: Watch One Job Spread
• 9:51 The Throttling Staircase, and Who Skips Steps
• 2:50 Demo: Read the Metrics App
• 12:46 Sizing From the Smoothed Line

Resources

• Fabric capacity throttling policy
• Smoothing and throttling in Fabric Data Warehouse
• Throttle triggers and throttle stages
• Capacity planning guide
• Autoscale Billing for Spark
• Fabric operations: interactive and background
• Understand the metrics app timepoint page
• Understand the metrics app timepoint summary page
• Surge protection
• Capacity and SKUs
• Burstable capacity in Fabric Data Warehouse
• Consumption rates and billing for Copilot in Fabric
• Azure quotas for Fabric capacities

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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Episode 23 • 2026-06-05 Duration: 10:49

Fabricの MLflow自動ログ記録は手間ゼロの実験追跡を約束します。ところがLightGBMやXGBoostで学習すると、メトリクスの列が空白のまま。フレームワークの溝、修正を飲み込むexclusiveフラグ、そしてバックアップにならないGit連携を分解します。

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

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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Episode 36 • 2026-10-02 Duration: 9:54

ワークスペース1つで4つの境界線が生まれます。セキュリティ、コスト、リージョン、ライフサイクル。設定したのはそのうち1つだけ。Fabricで「読み取り専用」が実際に何を意味するのか、OneLakeセキュリティが共同作成者を制限できない理由、そしてロール昇格のたびに静かに起きるセキュリティ変更を掘り下げます。

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 architectural principle to take home

Watch the episode

The Fabric Friday video on this topic: https://youtu.be/tpePwivxtzk

From the video

• 6:25 Demo: Same Table, Two Routes
• 12:28 Demo: Watch the Override
• 14:35 Who Can Claim Your Workspace

Resources

• Roles in workspaces
• Permission model
• Give users access to workspaces
• OneLake security access control model
• Capacity reassignment restrictions
• Understand Fabric licenses and capacity
• Manage workspaces
• What is a lakehouse
• Reassign a workspace to a different capacity
• Create a workspace
• Workspace roles in Fabric Data Warehouse
• Workspace roles in Lakehouse
• Roles in workspaces in Power BI
• Build permission for shared semantic models
• Get started with Microsoft Fabric

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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Episode 22 • 2026-05-29 Duration: 11:08

Fabric ML ModelはMLflowレジストリをワークスペース内に組み込み、バッチスコアリングからPower BIへの表示まで更新なしで完結する。しかしリアルタイムエンドポイントはプレビュー、Gitはメタデータしか追跡せず、GPUもない。Azure MLを本当に置き換えられるのか、それとも共存させるべきなのか。

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
• When the rejected approach is actually right
• F-SKU realism — what this actually costs
• The concrete recommended architecture
• Risks of the recommended path
• The architectural principle to take home

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

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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Episode 21 • 2026-05-22 Duration: 13:30

FabricのSchema Registryは型安全なストリーミングを約束します。しかし、スキーマに合わないイベントはデッドレターキューもなく消え、スキーマ更新時の互換性チェックもなく、稼働中のEventstreamにスキーマモードを後付けすることもできません。この契約が実際に何を強制し、何を黙認しているのかを掘り下げます。

What we discuss

• How it actually works underneath the abstraction
• Where the obvious answer breaks
• The pattern we keep seeing in the field
• 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

Watch the episode

The Fabric Friday video on this topic: https://youtu.be/dY7fGdiP9dE

Resources

• Schema Registry — known limitations
• CloudEvents 1.0
• Use schemas in eventstreams
• Real-Time Hub Schemas
• Business Events Concepts
• Consume Business Events from Activator
• Eventhouse
• Confluent Kafka source
• Schema Registry in Fabric Real-Time Intelligence (preview) — Overview
• Create and manage event schema sets
• Create and manage event schemas in schema sets
• EventSchemaSet REST API definition
• Eventstream Overview — Schema Management section
• Multiple-Schema Inferencing in Eventstream (Preview)
• Eventstream Data Formats: JSON, CSV, Avro

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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Episode 20 • 2026-05-15 Duration: 11:51

Fabric Activatorのノーコードアラートは、表面上はシンプルです。しかしその下のエンティティモデルには、どのチームも一度は引っかかる課金の仕組みが隠れています。停止ボタンを押してもメーターが止まらない理由を、徹底的に分解します。

What we discuss

• How it actually works underneath the abstraction
• Where the obvious answer breaks
• 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
• Risks of the recommended path
• The architectural principle to take home

Watch the episode

The Fabric Friday video on this topic: https://youtu.be/ceJoY7YxWvk

Resources

• Add Activator to Eventstream
• Activator from KQL Queryset
• Activator from RTD
• Activator from Power BI
• Real-Time Hub Set Alerts
• Set Alerts on Anomaly Detection
• What is Fabric Activator?
• Tutorial: Create and activate a Fabric Activator rule
• Create a rule in Fabric Activator
• Trigger modeling in Activator
• Fabric Activator rules
• Detection conditions
• Activator Limitations
• Latency in Activator
• Activator Capacity Consumption

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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Episode 19 • 2026-05-08 Duration: 13:22

リアルタイムダッシュボードはすべてのタイルにアラートボタンを備え、保存のたびに「成功」と返します。Kusto直結の監視画面がどうクエリを実行しているか、権限モデルが閲覧とクエリ実行をどう分離しているか、そしてアラートのパイプラインがどこで黙って評価を停止するかを追います。

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
• 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

Watch the episode

The Fabric Friday video on this topic: https://youtu.be/A_AFcP40K8E

Resources

• KQL Database
• KQL Queryset
• Real-Time Hub
• Activator on RTD
• Anomaly Detection
• Power BI + KQL
• Fabric Map
• What is Real-Time Dashboard?
• Create a Real-Time Dashboard
• Real-Time Dashboard Permissions
• Use Parameters in Real-Time Dashboards
• Customize Real-Time Dashboard Visuals
• Activator Limitations
• Generate Real-Time Dashboard with Copilot
• Copilot-assisted Real-Time Data Exploration (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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Episode 17 • 2026-04-24 Duration: 12:50

多くのチームはexplainキーワードでKQLを学びます。SQLを構文的に正しいKQLに変換してくれる便利な機能ですが、変換できるのはSQLで表現可能な範囲だけです。binとsummarizeの組み合わせとmake-seriesの構造的な違いを掘り下げ、ほとんどのチームがたどり着かない異常検知が実は演算子ひとつ先にある理由を解き明かします。

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

Watch the episode

The Fabric Friday video on this topic: https://youtu.be/8amqWDeHT2Y

Resources

• Query data in a KQL queryset
• Create a KQL queryset
• Kusto Query Language overview
• SQL to KQL cheat sheet
• KQL quick reference
• make-series operator
• series_decompose_anomalies()
• Anomaly detection and forecasting
• Time series analysis
• render operator
• Share KQL queries
• Create a Real-Time Dashboard
• Real-Time Intelligence tutorial part 5: Query streaming data using KQL
• Tutorial: Learn common operators
• Tutorial: Use aggregation functions

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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Fabric Architecture Podcast — 日本語

AI-generated voices. Matthias — cloned voice. Fabia — designed AI co-host.

Based on the Fabric Periodic Table.

New episodes every Friday. DM Matthias on LinkedIn.

AI-generated voices. Brand design based on fabricperiodictable.com.

Episode 16 • 2026-04-17 Duration: 11:57

すべてのKQL Databaseには、既定で10年分のホットSSDキャッシュが有効になっています。Fabricに時系列データ専用エンジンが必要な理由を掘り下げ、コストとパフォーマンスを制御する2つのポリシーを解きほぐし、効率的なストアを高額にしてしまう設定ミスを解説します。

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
• 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

Watch the episode

The Fabric Friday video on this topic: https://youtu.be/z57H_Zqvbkc

Resources

• What is Real-Time Intelligence?
• Choose an analytical data store in Microsoft Fabric
• Eventhouse overview
• Data connectors overview
• Get data overview
• Change data policies
• KQL overview - scalar data types
• Eventhouse and KQL Database consumption
• Pricing cost drivers
• Create a KQL database
• Time series analysis
• Anomaly detection and forecasting
• Manage and monitor a database
• Manage and monitor an eventhouse
• KQL Database git integration

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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Episode 15 • 2026-04-10 Duration: 12:37

Eventstreamはキャンバス上で正常を示しながら、裏でイベントを静かに破棄します。ノーコードの抽象化を分解し、3つのサイレント障害パターンをたどり、FinOpsチームを悩ませる4つの課金メーターと向き合います。

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
• 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

Watch the episode

The Fabric Friday video on this topic: https://youtu.be/oWEPB9q-rMs

Resources

• Eventstream Overview
• Add and manage event sources
• Route events to destinations
• Edit and publish an eventstream
• Route data streams based on content
• DeltaFlow output transformation
• Monitor the status and performance of an eventstream
• Pause and resume data streams
• Capacity consumption for Fabric eventstreams
• Add Azure Event Hubs source
• Add Azure IoT Hub source
• Add Eventhouse destination
• Add Lakehouse destination
• Process events with SQL code editor
• Explore and transform bike-sharing data

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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Episode 14 • 2026-04-03 Duration: 12:40

FabricのKQLデータベースには、10年分のホットキャッシュが既定で設定されています。このエピソードでは、Eventhouseの課金の仕組み、アップタイムメーター、キャッシュストレージの階層、そして高速エンジンを想定外の請求書に変えない構成の選び方を解説します。

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
• F-SKU realism — what this actually costs
• The concrete recommended architecture
• When the rejected approach is actually right
• Risks of the recommended path
• The architectural principle to take home

Watch the episode

The Fabric Friday video on this topic: https://youtu.be/IxdoLrtDykM

Resources

• Eventhouse overview
• Store data in Microsoft Fabric - Decision guide
• Caching policy (hot and cold cache)
• Get data overview
• KQL quick reference
• Eventhouse and KQL Database consumption
• Data availability in OneLake
• Enable Eventhouse endpoint for lakehouse and data warehouse
• Create an Eventhouse
• Create a KQL database
• Real-Time Intelligence tutorial
• Change data policies
• Cost breakdown of Eventhouse
• Manage and monitor an eventhouse
• Decision guide: Choose the right data store

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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Episode 13 • 2026-03-27 Duration: 13:24

Apache Airflow ジョブは、PythonのDAGによるコードファーストのオーケストレーションをFabricに持ち込みます。ただし常時稼働の計算モデルは、3つのノードが24時間回り続けることを意味します。このトレードオフが正当化される場面はどこか。Data Pipelineが正直な答えになるのはいつか。

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
• Where the obvious answer breaks
• When the rejected approach is actually right
• The concrete recommended architecture
• Risks of the recommended path
• The architectural principle to take home

Key takeaways

• Apache Airflow ジョブは、2026年1月に廃止されたAzure Data FactoryのWorkflow Orchestration Managerの後継です。FabricのData Factoryワークロード内で動くマネージドSaaS版のAirflowで、バージョンは2.

Watch the episode

The Fabric Friday video on this topic: https://youtu.be/21mzNmKqBuo

Resources

• What is Apache Airflow Job?
• Run a Fabric item using Apache Airflow DAGs
• Apache Airflow compute in Fabric
• Apache Airflow job pricing
• Transform data using dbt
• Migrate to Apache Airflow job in Microsoft Fabric
• CI/CD for Apache Airflow in Fabric
• Apache Airflow Job region availability
• Quickstart: Create an Apache Airflow Job
• Apache Airflow Job workspace settings
• Access Apache Airflow Job Logs
• Sync a GitHub repository in Apache Airflow Job
• Run Hello-world DAG in Apache Airflow Job
• Fabric region availability
• MS Learn

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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Episode 12 • 2026-03-20 Duration: 12:19

コピージョブは、Data Pipelineの柔軟性とミラーリングの手軽さを兼ね備えると謳われています。ところが増分モードではCU単価が2倍になり、V-Order圧縮やテーブル単位の切り上げが重なります。そのコスト構造を1つずつ計算します。

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
• The concrete recommended architecture
• Risks of the recommended path
• The architectural principle to take home

Watch the episode

The Fabric Friday video on this topic: https://youtu.be/JHLYx4jyvVQ

Resources

• Copy Job Connectors
• CDC Replication Connectors
• VNet Data Gateway for Copy Job
• CI/CD for Copy Job
• Copy Job Activity
• What is Copy Job in Data Factory?
• Create a Copy Job
• Quickstart: Create a Copy Job
• CDC in Copy Job (Preview)
• Copy Job Pricing
• Monitor a Copy Job
• Data Movement Decision Guide
• Data Integration Decision Guide
• What is Data Factory in Fabric?
• Pricing Scenario: Copy Job 1 TB CSV to Lakehouse

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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Episode 11 • 2026-03-13 Duration: 12:38

SQL Database in Fabricは、ETLなしでOLTPと分析を1つのデータベースから実現すると謳います。しかし対話型CU課金モデルは、小規模な容量ではその約束を高くつかせます。どこで算数が合い、どこで合わないかを正確に分解します。

What we discuss

• How it actually works underneath the abstraction
• Where the obvious answer breaks
• The pattern we keep seeing in the field
• A real Reddit/Microsoft Q&A question unpacked
• 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

• SQL Database in Fabricは、Azure SQL Databaseと同じエンジンで動いています。T-SQLの対応範囲も同じ、インテリジェントクエリ処理も同じ、.

Watch the episode

The Fabric Friday video on this topic: https://youtu.be/Jv0zQBB2Fd0

Resources

• SQL database in Microsoft Fabric — Overview
• Mirroring Fabric SQL database
• Translytical applications with SQL database
• SQL database in Fabric tutorial — End-to-end architecture
• Data Factory SQL database connector
• GraphQL API for SQL database
• Mirroring limitations
• SQL analytics endpoint
• Automatic Tuning
• AI functions
• Fabric's CI/CD framework
• Limitations in SQL database in Fabric
• Feature comparison: Azure SQL Database and Fabric SQL database
• Billing and utilization reporting for SQL database
• Mirroring Azure SQL Database to Fabric

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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Episode 10 • 2026-03-06 Duration: 13:12

SQL 分析エンドポイントはすべてのLakehouseにT-SQLアクセスを即座に提供します。しかしメタデータ同期は15分の無操作で停止し、T-SQLから鮮度を確認する手段はなく、ビューはPower BIをDirect LakeからDirectQueryへ無言で切り替えます。目を離すと暗転する読み取り窓の話。

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
• The concrete recommended architecture
• F-SKU realism — what this actually costs
• When the rejected approach is actually right
• The architectural principle to take home

Watch the episode

The Fabric Friday video on this topic: https://youtu.be/te3TjK0z4uI

Resources

• What is the SQL analytics endpoint for a lakehouse?
• SQL analytics endpoint — Capabilities
• Decision guide: Warehouse and Lakehouse
• Refresh SQL endpoint metadata
• Data types in the SQL analytics endpoint
• Direct Lake overview
• Share a Lakehouse
• Lakehouse overview
• Warehouse overview
• SQL Database in Fabric
• OneLake shortcuts
• Database mirroring
• OneLake security
• Cross-database queries
• Better together: the lakehouse and warehouse

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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Fabric Architecture Podcast — 日本語

AI-generated voices. Matthias — cloned voice. Fabia — designed AI co-host.

Based on the Fabric Periodic Table.

New episodes every Friday. DM Matthias on LinkedIn.

AI-generated voices. Brand design based on fabricperiodictable.com.

Episode 8 • 2026-02-20 Duration: 12:56

Fabricのミラー化データベースは、SQL Server、Cosmos DB、Snowflakeからゼロコードでほぼリアルタイムの複製を約束します。MatthiasとFabiaがCDCの内部動作を解剖し、複製は無料でもクエリは有料という構造を明かし、通知なしにミラーを壊すスキーマ変更を追跡します。

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
• 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

Watch the episode

The Fabric Friday video on this topic: https://youtu.be/rOvhLsUYDTI

Resources

• Mirrored databases overview
• Azure SQL DB mirroring
• Azure SQL DB limitations
• SQL Managed Instance limitations
• SQL Server limitations
• Cosmos DB mirroring
• Cosmos DB limitations
• Snowflake mirroring
• Snowflake limitations
• Troubleshooting

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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Episode 7 • 2026-02-13 Duration: 13:20

パイプラインは正常に完了しました。ステータスは「成功」。でも、Lakeouseのテーブルは空のまま。FabricのData Pipelineが返す「成功」の本当の意味と、空振り実行を検知する検証アーキテクチャ、そしてパイプラインを使わないほうがいい場面を、MatthiasとFabiaが解説します。

What we discuss

• How it actually works underneath the abstraction
• 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

Watch the episode

The Fabric Friday video on this topic: https://youtu.be/6A6ZlyIuvkw

Resources

• Data Pipelines in Fabric
• Activity Overview
• Control flow activities
• Pipeline Overview
• Copy Activity
• Parameters and Expressions
• Monitor Pipeline Runs
• Decision Guide: Pipeline vs Dataflow vs Spark
• Data Integration Strategy Guide
• Reddit r/MicrosoftFabric
• MS Q&A
• Stack Overflow

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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Episode 6 • 2026-02-06 Duration: 12:19

スケジュール実行のノートブックが「変数が定義されていません」で断続的に失敗する。実はそれが幸運な結果です。不運なほうは、古い状態のまま実行され、もっともらしい数字を出力し、1か月間誰も気づきません。

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
• The concrete recommended architecture
• Risks of the recommended path
• The architectural principle to take home

Watch the episode

The Fabric Friday video on this topic: https://youtu.be/7oEBYgYZNrk

Resources

• What is a Spark Job Definition?
• Create SJD
• Run SJD
• SJD Git Integration
• Pipeline SJD Activity
• Streaming Data into Lakehouse
• Spark Best Practices
• Job Queueing
• Decision Guide: Pipeline vs Dataflow vs Spark
• Fabric vs Synapse Spark Comparison
• MS Q&A
• Reddit r/MicrosoftFabric
• Stack Overflow

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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Episode 5 • 2026-01-30 Duration: 11:36

Fabric環境を使わないチームは、スケジュール済みノートブックの毎回のセッションで%pip installを実行し続ける。誰もが不満を言う12分の発行時間は、実はSpark予算のなかで最も安い項目だった。

What we discuss

• How it actually works underneath the abstraction
• A real Reddit/Microsoft Q&A question unpacked
• F-SKU realism — what this actually costs
• Where the obvious answer breaks
• When the rejected approach is actually right
• The concrete recommended architecture
• Risks of the recommended path
• The architectural principle to take home

Watch the episode

The Fabric Friday video on this topic: https://youtu.be/jlhWv3gcsV4

Resources

• Create and manage environments
• Configure Starter Pools
• Manage libraries
• Environments GA
• Reddit r/MicrosoftFabric
• MS Q&A
• Stack Overflow

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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Episode 4 • 2026-01-23 Duration: 11:22

Dataflow Gen2はPower Queryの裏側で、ステージングLakehouseを自動的に作成しています。実行アーキテクチャを掘り下げ、クエリフォールディングがパフォーマンスを左右する理由を解説し、ノートブックに切り替えるべきタイミングを見極めます。

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
• The concrete recommended architecture
• F-SKU realism — what this actually costs
• When the rejected approach is actually right
• The architectural principle to take home

Watch the episode

The Fabric Friday video on this topic: https://youtu.be/MikCyAEmsis

Resources

• What is Dataflow Gen2?
• Dataflow Gen2 architecture
• Connector overview
• Power Query M reference
• Staging settings
• Incremental refresh
• Schedule Dataflow Gen2
• Best practices
• Create your first Dataflow Gen2
• Monitor Dataflows

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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Episode 3 • 2026-01-16 Duration: 12:10

Fabricのノートブックは手軽なコーディング環境に見えますが、裏側にはドライバーノード境界という誰も警告してくれない分散システムが潜んでいます。パイプラインに載せたとき何が変わるのか、そしてSpark Job Definitionへの移行が片づけの手間に見合うタイミングはいつか、紐解いていきます。

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

Watch the episode

The Fabric Friday video on this topic: https://youtu.be/FUAQZEhQtFI

Resources

• What is a notebook?
• Python experience in notebooks
• Configure Starter Pools
• Lakehouse and notebooks
• Data Wrangler
• Manage libraries
• Schedule notebook
• High Concurrency mode
• VS Code extension
• Spark Job Definition
• Notebook best practices
• Author and execute notebooks
• Notebook visualization

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.

このポッドキャストは AI によって生成されています。どちらの音声も合成です。Matthias はクローン音声、Fabia は設計された AI 共同ホストです。

Brand design based on fabricperiodictable.com.

Fabric Architecture Podcast — 日本語

AI-generated voices. Matthias — cloned voice. Fabia — designed AI co-host.

Based on the Fabric Periodic Table.

New episodes every Friday. DM Matthias on LinkedIn.

AI-generated voices. Brand design based on fabricperiodictable.com.

Episode 1 • 2026-01-02 Duration: 10:28

Fabric のレイクハウスは、設定したら終わりのデータプラットフォームではありません。Matthias と Fabia が、メダリオン層、Deltaテーブルのメンテナンス、Direct Lake、そしてウェアハウスの方が正解なケースなど、リアルなアーキテクチャのトレードオフを掘り下げます。

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

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.