Episode 43 • 2026-10-30 Duration: 9:02

A sensitivity label says Highly Confidential in the header, but inside Fabric it never decides who opens the item. This episode traces where labels actually control access and where the appearance of a lock is just a sign.

What we discuss

• The pattern we keep seeing in the field
• 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
• The concrete recommended architecture
• Risks of the recommended path
• The architectural principle to take home

Key takeaways

• Right now, somewhere, there's a lakehouse with Highly Confidential in the header.
• Show me the query pattern. If the pattern is "restrict who opens this," design the workspace. If the pattern is "protect what leaves," design the label. Conflating them is where every thread on that forum starts.
• And the protection policy sits in between.

From the video

• Demo 1: The Label Changed Nothing
• Architecture film: Two questions two mechanisms
• Architecture film: Eight routes onto an item
• Architecture film: Where protection starts
• Architecture film: Where label and permission meet

Resources

• Sensitivity labels in Power BI
• Sensitivity labels troubleshooting
• Protected sensitivity labels in Fabric and Power BI
• Roles in workspaces
• Permission model
• Row-level security (RLS) with Power BI
• Information protection in Microsoft Fabric
• Protection policies in Microsoft Fabric
• Restrict access to content by using sensitivity labels to apply encryption
• Export data from a Power BI visualization
• Apply sensitivity labels to Fabric items
• Default label policy for Fabric and Power BI
• Mandatory label policy for Fabric and Power BI
• Domain-level default sensitivity labels
• Information protection 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.

This podcast was generated by AI. Both voices are synthetic: Matthias is a cloned voice, Fabia is a designed AI co-host.

Brand design based on fabricperiodictable.com.

Episode 42 • 2026-10-23 Duration: 10:17

Medallion is the recommended design approach for Fabric. It's also one building block out of five, and the one that sends you a maintenance invoice nobody put on the architecture diagram. What the file-count guardrail means for Direct Lake on OneLake, why schema-enabled lakehouses can't use the automated maintenance activity, and when the whole pattern is overkill.

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

Key takeaways

• Somewhere right now, there's a table sitting at four thousand files on an F64.
• The price of that answer is maintenance. A schedule, a monitor, and someone who knows where TABLETRAITS lives.
• Architecture is not religion. Medallion answers one question — what is each stage trusted for — and it leaves the other four to the other four bricks.

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.

This podcast was generated by AI. Both voices are synthetic: Matthias is a cloned voice, Fabia is a designed AI co-host.

Brand design based on fabricperiodictable.com.

Episode 41 • 2026-09-18 Duration: 8:45

Databricks and Fabric have five integration paths, not one. Each moves data. None moves permissions. We pull apart the one-credential model, the hour-long revoke delay, and the ADLS shortcut that silently bypasses Unity Catalog's row-level security.

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

Key takeaways

• Every path between these platforms moves data.
• Somewhere right now, a mirrored catalog is serving a workspace that was supposed to see two schemas.
• And accept that "keep access configurations synchronized" is documentation for there is no sync.

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.

This podcast was generated by AI. Both voices are synthetic: Matthias is a cloned voice, Fabia is a designed AI co-host.

Brand design based on fabricperiodictable.com.

Episode 37 • 2026-10-16 Duration: 9:21

Fabric doesn't charge you when work happens — it spreads the cost forward into 2,880 thirty-second timepoints. That one sentence explains every confusing capacity behavior, including why surge protection can reject the SQL it's supposed to protect.

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

Key takeaways

• And someone's about to halve the SKU because the chart looks calm.
• The spike they see is a receipt. The smoothed line is the bill that hasn't arrived.
• Right now, somewhere, there's a capacity chart with a quiet-looking evening.

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.

This podcast was generated by AI. Both voices are synthetic: Matthias is a cloned voice, Fabia is a designed AI co-host.

Brand design based on fabricperiodictable.com.

Episode 38 • 2026-10-09 Duration: 8:43

Fabric domains look like governance boundaries but the documentation says otherwise. We unpack what a domain actually gives you, the default-domain mechanism that's still running in your tenant, and why the label on the cabinet is not the lock.

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

Key takeaways

• There's a filing cabinet in every office with a label on the front.
• Pattern dictates platform. If the pattern is "organize and find," a domain is the right tool. If the pattern is "restrict and protect," you're in the wrong layer.
• So here's where I'd land. A domain answers "whose data is this." Use it for discovery and delegation. Build your actual boundaries with workspace roles and item permissions. And audit that default domain list quarterly, because it's still running.

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.

This podcast was generated by AI. Both voices are synthetic: Matthias is a cloned voice, Fabia is a designed AI co-host.

Brand design based on fabricperiodictable.com.

Episode 40 • 2026-09-25 Duration: 9:19

Cosmos DB has no schema. Delta tables need one. When mirroring bridges the two, the replicator guesses — and the guess only ever widens. We trace where the bytes come from, why renamed properties leave ghost columns full of nulls, and what stop replication actually does.

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

Key takeaways

• Somewhere right now a Cosmos container is taking writes.
• And match your Fabric capacity region to the Cosmos account.
• Three questions before you touch anything.

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.

This podcast was generated by AI. Both voices are synthetic: Matthias is a cloned voice, Fabia is a designed AI co-host.

Brand design based on fabricperiodictable.com.

Episode 36 • 2026-10-02 Duration: 8:03

A workspace carries four boundaries — security, cost, region, lifecycle — and you configured one of them. This episode pulls apart what 'read-only' actually means in Fabric, why OneLake security cannot fence in a Contributor, and the silent security change hiding inside every role promotion.

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

Key takeaways

• Somewhere right now, someone's promoting a colleague to Contributor so they can fix a report.
• Show me the query pattern. That tells you whether Viewer plus OneLake security is enough, or whether you need to split.
• Here's what I'd carry out of this. Every workspace is a cut through the lake, and cuts carry consequences. Security, cost, region, lifecycle — you're deciding all four whether you chose them or not.

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.

This podcast was generated by AI. Both voices are synthetic: Matthias is a cloned voice, Fabia is a designed AI co-host.

Brand design based on fabricperiodictable.com.

Episode 35 • 2026-08-28 Duration: 8:42

Graph in Fabric materializes a second copy of your data for traversal. That copy explains the staleness, the rebuild cycle, and the workspace boundary that decides whether graph belongs in your architecture.

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

Key takeaways

• And somewhere right now, there's a queryset returning a result that was true yesterday.
• A query timeout of twenty minutes. That's a ceiling you can plan around.
• What you can't plan around is the copy. It's stale between rebuilds, locked to the workspace, and if you query it carelessly it hands you a partial answer with a warning you didn't read. Accept those three things and it solves relationship...

From the video

• Architecture film: Snapshot not view
• Architecture film: Four ways to ask
• Architecture film: Staleness chain
• Demo 3: Change the Model, Watch the Queryset
• Architecture film: The ceilings

Resources

• How graph in Microsoft Fabric works
• Current limitations of graph in Microsoft Fabric
• Troubleshooting and FAQ for graph
• Quickstart: Create your first graph
• Tutorial: Introduction to graph
• Tutorial: Query the graph by using the query builder
• Example graph datasets
• Optimize GQL query performance
• GQL language guide
• GQL quick reference
• GQL query API (REST)
• Graph data models
• Design a graph schema
• Fabric Data Agent
• OneLake security

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. Both voices are synthetic: Matthias is a cloned voice, Fabia is a designed AI co-host.

Brand design based on fabricperiodictable.com.

Episode 34 • 2026-08-21 Duration: 8:36

A connectivity dialog in Fabric's API for GraphQL looks like a setup step. It quietly decides who needs permissions where, whether row-level security means anything, and whether your production API serves development data after every deployment.

What we discuss

• How it actually works underneath the abstraction
• 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

Key takeaways

• The thing that stays with me... a generated API feels managed. Like it's watching your source and keeping up.
• And the decision that matters most isn't which source to connect.
• Pattern dictates platform. If the pattern is an app team needing typed, authenticated access to warehouse data, this is the right tool.

Watch the episode

The Fabric Friday video on this topic: https://youtu.be/IuGCxyLWzo0

From the video

• 2:34 Demo: An API in Ninety Seconds
• 19:42 Demo: Schema Drift, Live
• 17:07 What Deployment Actually Does
• 14:48 Demo: Two Sources, One Query

Resources

• Fabric API for GraphQL FAQ
• Limitations of Microsoft Fabric API for GraphQL
• Connect AI Agents to Fabric API for GraphQL with a local MCP server
• What is Microsoft Fabric API for GraphQL?
• Create an API for GraphQL in Fabric and add data
• Connect applications to Fabric API for GraphQL
• Source control and deployment pipelines in API for GraphQL
• Fabric API for GraphQL editor
• Query multiple data sources in Fabric API for GraphQL
• Use service principals with Fabric API for GraphQL
• Expose stored procedures
• Performance best practices
• Create GraphQL API from your SQL database
• Develop GraphQL applications in Visual Studio Code
• Private Links overview

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. Both voices are synthetic: Matthias is a cloned voice, Fabia is a designed AI co-host.

Brand design based on fabricperiodictable.com.

Episode 33 • 2026-08-14 Duration: 8:26

An org app is a Fabric item that manages permissions on other items. We pull apart what it actually propagates, where the abstraction stops, and why some grants outlive the authority that created them.

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
• 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 there's a semantic model in workspace B with a permission entry that nobody in workspace A can remove.
• The sentence to say before you build one: the org app handles THESE permissions.
• An org app is an item that manages permissions on other items.

From the video

• Architecture film: An item not a publication
• Architecture film: Propagation tree
• Demo 2: Grant, Inspect, Revoke
• Architecture film: The hidden content switch

Resources

• Get started with org apps
• Collaborate with Power BI in Teams
• CI/CD for org apps in Fabric
• How to integrate notebooks with Org app
• Share Fabric Maps: Org apps
• Workspace roles in Microsoft Fabric
• Publish an app in Power BI
• Apps in Power BI (consumer view)
• Direct Lake overview – permission requirements
• Email subscriptions for reports
• Get started with Git integration
• Source code format – automatically generated system files
• Focus mode in Fabric
• Fabric preview features
• Power BI add-in for PowerPoint

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. Both voices are synthetic: Matthias is a cloned voice, Fabia is a designed AI co-host.

Brand design based on fabricperiodictable.com.

Episode 32 • 2026-08-07 Duration: 8:22

A Power BI app update succeeds and every consumer sees nothing. The publish boundary copies more than you think, grants less than you expect, and fails without a sound.

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

Key takeaways

• Somewhere right now, an app update just succeeded.
• The workspace app is built around one idea — publishing makes a copy.
• My advice: if you need the gate, accept what the gate costs.

From the video

• Architecture film: The publish boundary
• Architecture film: Permission arithmetic
• Demo 2: The Hidden-by-Default Trap
• Demo 3: The Pipeline Moves the Content, Not the App

Resources

• Apps in Power BI
• Get started with org apps
• Publish an app in Power BI – Considerations and limitations
• Power BI enhanced report format (PBIR)
• Collaborate with Power BI in Teams
• Workspaces in Power BI
• Create a workspace in Power BI
• End-to-end: From raw data to a shared Power BI app
• The deployment pipelines process
• Best practices for lifecycle management in Fabric
• App tenant settings
• What are Power BI template apps?
• Create a template app in Power BI
• Create your first Fabric app
• Strategy for using groups

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. Both voices are synthetic: Matthias is a cloned voice, Fabia is a designed AI co-host.

Brand design based on fabricperiodictable.com.

Episode 31 • 2026-07-31 Duration: 10:32

A scorecard silently creates its own semantic model the moment you hit New. That one architectural fact — that it's a stateful writer, not a read-only lens — explains why RLS doesn't carry over, why refresh breaks upstream, and why rollups leak across permission boundaries.

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
• The concrete recommended architecture

Key takeaways

• Somewhere right now, a scorecard with forty goals is showing all-green because nobody's checked in since March.
• So the lesson from all of this — a scorecard's the right tool when you need to know who promised what, and what they said when it slipped.
• That's the correct choice for more teams than you'd expect.

Resources

• Get started with goals
• Deprecation of Metric Sets in Power BI
• Fabric Community updates blog
• Create scorecards
• Scorecard subscriptions
• Create subgoals
• Hierarchies in scorecards
• Create connected goals in Power BI
• Stay on top of your goals (check-ins)
• Create automated status rules for goals
• Protect your scorecards with goal-level permissions
• Create linked goals in the Power BI service (preview)
• Refresh goals and scorecards automatically
• Use Power Automate to update goals automatically
• Use Power BI metrics to track goals in Microsoft Teams

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.

Episode 30 • 2026-07-24 Duration: 10:13

Paginated reports carry no data model, and that one design decision explains every limit teams hit in production. This episode traces the consequences from silent data drops to a tenant-wide security door that only opens one way, and lands on the question that makes the tool choice for you.

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

Key takeaways

• And a Diagnostics button nobody's pressed is sitting there, ready to explain why.
• Somewhere right now, a Monday-morning subscription is carrying a Weekday expression nobody remembers, heading for a ceiling that used to be a slope and is now a cliff.
• Show me the query pattern. If the aggregation's in the right place, the report scales. If it isn't, no capacity tier saves you. That's the decision that actually matters.

Resources

• What are paginated reports in Power BI?
• When to use paginated reports
• Paginated reports in the Power BI service
• Performance and scalability
• FAQ
• Paginated Report definition
• Access the paginated report editor
• SQL Server Reporting Services (Wikipedia)
• RDL Schema Specification
• Plan to migrate .rdl reports
• Supported data sources
• Connect via Power Query
• Create and use the paginated report visual
• Email subscriptions
• Export Power BI paginated reports

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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

• 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...

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

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.

Episode 20 • 2026-05-15

Data Activator is Fabric's no-code event detection engine — but most teams build it wrong. Matthias and Fabia unpack the stateful rule model, the billing trap everyone hits once, and when Power Automate is actually the better answer.

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

• Take-home: the entity hierarchy is the product.
• Fair. For low-frequency data — a daily KPI check — it works fine. Where it breaks: ten thousand events per second per rule. Power Automate isn't built for that volume. And a per-flow variable isn't per-object state — you'd need one flow...
• Right. Wrong in exactly one place — the state machine. Here's the thing. A stateless rule fires on every matching event. Value greater than twenty-five? Sensor reports every five seconds, stays above twenty-five for an hour — you get seven...

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

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.

Episode 19 • 2026-05-08

Real-Time Dashboard is not Power BI wearing a different hat. Matthias and Fabia unpack the naming collision, permission separation, Activator alert traps, and when you should actually use Power BI DirectQuery instead.

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

• If someone asks 'what's happening right now' — Real-Time Dashboard.
• But you lose permission separation. You lose tile-as-query simplicity. And your team will absolutely blame the network when the DirectQuery report takes four seconds to load at scale. Different tools, different tradeoffs.
• Fair argument. Power BI can connect to KQL via DirectQuery. You get DAX measures, RLS, the full semantic model. And in Premium, automatic page refresh goes as low as five seconds. So if your team already lives in Power BI — that's a legitimate path.

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

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.

Episode 18 • 2026-05-01

Matthias and Fabia unpack Fabric's Real-Time Hub — the tenant-wide catalog that sits above Eventstream, Eventhouse, and Activator. They tackle why it feels redundant until it doesn't, dig into a real Reddit question about skipping the Hub entirely, and lay out the four-layer real-time stack every architect should internalize.

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

• So — today's lesson. The Hub is not a processing engine. It's not a new Eventstream. It's the inventory layer that streaming has always been missing. Pattern dictates platform — if your pattern is discovery at organizational scale, this is...
• I mean, fair question. If every stream you have lives in one workspace and one team owns them all — the Hub's discoverability value is close to zero. You already know what exists. Same if you're publishing streams to non-Fabric consumers...
• Right. And... that's actually fine for small setups. The connector list is identical — same Azure Event Hubs tile, same Kafka tile, same CDC tiles. Both paths end up creating an eventstream artifact. But here's the thing. Eventstream is...

Watch the episode

The Fabric Friday video on this topic: https://youtu.be/tZmw36djm9w

Resources

• managed private endpoint
• Eventstream Overview
• KQL Database
• Activator Overview
• Real-Time Dashboard
• Schema Sets
• Digital Twin Builder
• Real-Time Hub Overview
• Get Started with Real-Time Hub
• Supported Sources
• Add Azure Event Hubs Source
• Add Azure IoT Hub Source
• Get Azure Blob Storage Events
• Create Streams from Workspace Item Events
• Create Streams from OneLake Events

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.

Episode 17 • 2026-04-24 Duration: 9:39

Matthias and Fabia explore the KQL Queryset in Microsoft Fabric — why the pipe-forward mental model beats SQL for time-series data, when to use make-series vs bin+summarize, and the architectural decision between KQL Queryset, Notebooks, and the SQL endpoint.

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

• So — the lesson. Show me the query pattern. That's it. Don't pick your tool based on what you know. Pick it based on what the data needs. If you're doing time-series at scale, learn the pipe. It's worth it.
• I mean, fair question. If your workload is analytical reporting — quarterly trends, executive dashboards, scheduled refresh — Power BI connected through the SQL endpoint is probably the better path. You get a richer visualization library,...
• Right. And the naive answer is — just use the T-SQL endpoint, it supports SELECT statements. Which is true. But here's the thing. T-SQL on a KQL database is read-only DQL. SELECT only. No DDL, no management commands. And more importantly —...

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

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.

Episode 16 • 2026-04-17 Duration: 10:26

Matthias and Fabia explore why KQL Database exists alongside four other analytical stores in Microsoft Fabric. They unpack the Eventhouse-as-building mental model, the caching vs retention trap, and when you should — and shouldn't — choose KQL over SQL.

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

• If your data is time-series, logs, or telemetry — and your queries are always filtered by time — KQL Database isn't just an option.
• Fair. And honestly, if your team has strong Python skills and your latency tolerance is minutes, not milliseconds — Lakehouse plus notebooks is a legitimate path. You get the Spark ecosystem, ML libraries, broader tooling. I wouldn't fight...
• Right. And that matters for the reversal. Because the naive answer teams land on is: just put your IoT data in the Lakehouse. Delta Lake handles everything, right?

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

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.

Episode 15 • 2026-04-10 Duration: 8:03

Matthias and Fabia break down Fabric Eventstreams — the visual stream processor that replaces three Azure services with one canvas. They explore why green doesn't always mean flowing, tackle Kafka compatibility from a real Reddit question, and walk through the four billing meters that confuse every FinOps team.

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

• Eventstreams are not about replacing Spark or Kafka.
• Fair. If you need stateful ML inference mid-stream, Eventstreams won't do it — route to a Spark Notebook destination instead. And if your team needs exactly-once semantics, at-least-once with deduplication in Eventhouse covers most cases,...
• And your team will absolutely say that in the sprint demo.

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

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.

Episode 14 • 2026-04-03 Duration: 7:32

Matthias and Fabia break down Microsoft Fabric's Eventhouse — when you need a dedicated real-time store, how hot and cold cache tiers drive your bill, and why the default cache policy is the most common Eventhouse mistake.

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
• The architectural principle to take home

Key takeaways

• Don't default your real-time data into the Lakehouse just because it's familiar.
• Completely. If your latency tolerance is five to thirty seconds and you already know Spark — that's a defensible architecture. Eventhouse costs you KQL ramp-up, a separate billing model, one more item to govern. Don't add an engine just...
• Right. Now — the naive answer when someone asks 'where do I put streaming data in Fabric' is always the Lakehouse. Everything goes to OneLake. Sounds clean on a slide.

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

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.

Episode 13 • 2026-03-27 Duration: 8:19

When should you reach for Apache Airflow Job instead of Data Pipelines in Microsoft Fabric? Matthias and Fabia break down pool types, cost traps, dbt fragility, and the architectural threshold where visual orchestration breaks down.

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

• I'm stealing that. But yeah — match the orchestrator to the orchestration complexity. If your workflow fits on a visual canvas, keep it there. Airflow is for when Python is genuinely the clearest way to express your pipeline logic.
• For maybe... sixty, seventy percent of Fabric workloads, Data Pipeline is the right answer. Airflow earns its spot when you need cross-cloud orchestration, dbt models, complex dependency graphs, or your team already thinks in Python DAGs....
• You just paid for a permanent orchestra conductor to wave a baton at six musicians who already know the song.

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

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.

Episode 12 • 2026-03-20 Duration: 9:54

Copy Job fills the gap between Mirroring's zero-config simplicity and Pipeline's full orchestration control. We break down the architecture, the 2x incremental pricing controversy, V-Order performance traps, and when you should still reach for Pipeline Copy Activity instead.

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

• Take-home principle. Copy Job is Copy Activity with built-in memory. Same engine, same connectors — it just remembers where it left off. Start with a full load to validate your schema. Switch to incremental once you trust it. And always —...
• And that's the counterpoint to the counterpoint.
• Fair question. For a team that already has mature pipeline patterns — control tables, parameterized watermarks, solid error handling — that is a legitimate choice. You pay one-point-five CU across the board and get ForEach loops,...

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

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.

Episode 11 • 2026-03-13 Duration: 9:50

SQL Database in Fabric promises zero-ETL translytical architecture — OLTP writes mirrored to OneLake automatically. But the 15-minute billing window and interactive CU consumption change the math. We break down when it makes sense and when Azure SQL Database plus mirroring is the smarter play.

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

• That's... a genuinely good architecture. For a lot of teams, that's the right call. You lose zero-config mirroring — you set it up manually — and you lose Git integration and the GraphQL API. But you gain VNet support, CDC, Always...
• Every. Single. Time. Plus the engine startup needs a two-gig minimum memory allocation. On an F2 capacity, that's... roughly your entire capacity eaten by one database.
• Technically — yes, that works. But the failure mode I asked about? It's cost. When you create a SQL Database in Fabric, two items appear — the database and a SQL analytics endpoint. Both consume interactive CUs. Not background —...

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

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.

Episode 10 • 2026-03-06 Duration: 10:53

The SQL Analytics Endpoint gives you T-SQL on Lakehouse data for free — but free comes with metadata sync delays, no Git integration, and silent Direct Lake fallbacks. Matthias and Fabia unpack when to use it, when to reach for a Warehouse instead, and how to avoid the traps that catch most teams.

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

• So — the take-home. The SQL Analytics Endpoint is a projection, not a database. Use it for what it does brilliantly — SQL access to Delta tables with zero data duplication. But the moment you create your first view, the moment you need...
• Fair point. And for teams that are SQL-first — BI developers, analysts who live in T-SQL — the Warehouse-only approach has real merit. You lose the zero-setup convenience, you manage a separate item, you pay for writes. But you gain...
• Hm, let me think... because free has a price. The endpoint runs a background metadata sync process that pauses after fifteen minutes of inactivity. Your next query wakes it up, but now you're waiting for a full resync. And if you've got a...

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

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.

Episode 9 • 2026-02-27 Duration: 15:34

Matthias and Fabia dissect the Warehouse-vs-Lakehouse decision. They explore why three SQL options exist in Fabric, when the SQL endpoint's read-only design saves you, and why judging Warehouse performance on a cold-cache first query is the mistake everyone makes.

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

• Today's takeaway. Warehouse and Lakehouse aren't competing products. They're different engines on the same storage. Pick based on workload pattern — T-SQL, multi-table transactions, auto-optimization means Warehouse. Spark, ML,...
• Fair. And honestly, for a lot of teams that works. If your reporting is read-only — dashboards, DirectLake — the SQL endpoint handles it fine. In a team of eight under cost pressure, Lakehouse plus SQL endpoint might be all you need. The...
• Right. So here's where people get it wrong. The naive answer is — Warehouse is for SQL people, Lakehouse is for Spark people. Pick your tribe. But that's way too simple. The real difference isn't the language you write. It's who owns the...

Watch the episode

The Fabric Friday video on this topic: https://youtu.be/hnADNoZcm14

Resources

• Warehouse in Fabric
• Create Warehouse
• T-SQL surface area
• Performance Guidelines
• Better together: Lakehouse and Warehouse
• Decision Guide: Warehouse vs Lakehouse
• SQL Analytics Endpoint Performance
• Result Set Caching
• Data Clustering
• Zero-Copy Table Clone
• Time Travel
• Transactions
• Migration from Synapse
• Architecture
• Warehouse Team AMA (March 2025)

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.

Episode 8 • 2026-02-20 Duration: 8:18

Matthias and Fabia explore Fabric mirrored databases — the sweet spot between real-time eventstreams and batch ETL. They unpack CDC replication, the 500-table limit, schema change risks, and why setup simplicity doesn't excuse you from data modeling.

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

• Today's takeaway — mirroring is the sweet spot between real-time eventstreams and batch ETL.
• Now — let me steel-man the alternative. Hm, let me think... If your analytics can tolerate live remote queries, shortcuts are genuinely better. No data copy, no replication lag, no storage cost. In a team of eight under cost pressure,...
• Kind of, yeah. The trap is skipping the data modeling conversation because setup was easy. You still need a medallion layer on top. Mirror gives you bronze — that's it. And your team will absolutely blame the mirror when queries are slow,...

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

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.

Episode 7 • 2026-02-13 Duration: 9:53

Matthias and Fabia debate when Fabric Data Pipelines earn their complexity. They unpack the orchestra-conductor mental model, walk through a real green-checkmark-but-no-data failure, and steel-man the case for skipping pipelines entirely.

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

• That's it. One activity, no dependencies — schedule it directly. Multiple steps with conditional logic — pipeline. But always validate outputs. Never trust the green checkmark without checking row counts.
• Completely fair. If you have existing ADF, keep using it. ADF connects to Fabric natively — orchestrate Fabric items from ADF, no problem. You get the richer connector library, managed private endpoints today, event-based triggers. For...
• And your team will absolutely add a pipeline anyway because it feels more 'enterprise.

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

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.

Episode 6 • 2026-02-06 Duration: 10:23

Matthias and Fabia break down Spark Job Definitions in Microsoft Fabric — the production wrapper most teams skip until their scheduled notebook fails at 3 AM. They cover the notebook-to-SJD promotion path, why state leakage kills overnight runs, retry policies, and when a plain notebook schedule is actually fine.

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

• And wrap it in a Pipeline. Even if just for the alerts.
• Fair. Solo workflows where the output includes visualizations — scheduled notebook is the right call. The line I draw: the moment a second person depends on that output, or it feeds a downstream system, promote it. Solo analyst with a...
• Every SJD run starts clean. No leftover variables, no stale session. That's — I mean, that's the whole point. Reproducibility by design, not by discipline.

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

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.

Episode 5 • 2026-01-30 Duration: 9:09

Matthias and Fabia dig into Spark Environments in Microsoft Fabric — the dependency management layer most teams adopt too early. They cover the Starter Pool trade-off, why publishing takes fifteen minutes, the SJD gotcha, and when percent-pip install is actually the right call.

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

• Fair. For pure experiments, percent-pip is the right call — session-scoped, instant, no friction. But across a team? Five people installing different versions every morning. No persistence between sessions. And good luck getting consistent...
• They just added fifteen minutes to every first session for zero benefit.
• Because of the publish step. When you create or update an Environment, Fabric resolves every dependency, downloads packages, builds what's basically a container image, and distributes it to compute nodes. That takes five to fifteen...

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

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.

Episode 4 • 2026-01-23 Duration: 11:20

Matthias and Fabia unpack Dataflow Gen2 — the low-code transformation layer in Fabric. They cover staging architecture, the Gen1-to-Gen2 leap, why your dataflow is slow, multi-destination patterns, and when you should skip the visual editor entirely and reach for a notebook.

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

• Low-code where you can, full-code where you must.
• Completely fair. And for teams with strong engineering backgrounds, notebooks are often the right call. But — here's the thing. Not every team has five Python engineers. I've worked with organizations where the data people are Excel and...
• They get Gen1 performance on a Gen2 label.

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

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.

Episode 3 • 2026-01-16 Duration: 11:26

Matthias and Fabia dig into Fabric Notebooks — the code-first data engineering workhorse. They cover starter pools vs. custom environments, the new native Python mode, the notebook-to-production gap, and why your notebook that runs perfectly at 2pm crashes in the pipeline at 2am.

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

• Completely fair. And I'll go further — I've seen teams spin up Spark to process ten megabytes of CSV. That's like renting a crane to hang a picture frame. If your transformation is row-level, SQL-expressible, and under a gig? A warehouse...
• Nope. It's one or the other. And your team will absolutely discover this at the worst possible moment — usually during a demo. The play is: develop and explore on starter pools, switch to a custom Environment only for final testing and...
• And teams do exactly that. Until they need great_expectations, or dbt-core, or some internal package. Starter pools give you the default library set — pandas, scikit-learn, plotly, the usual. But the moment you need anything custom, you...

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

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.

Episode 2 • 2026-01-09 Duration: 9:21

Matthias and Fabia break down OneLake shortcuts — virtual pointers that unify multi-cloud data without copies. They cover the read-write boundary, the shared credential security model, caching economics, and when a good old copy is actually the better architecture call.

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

• That's it. Don't shortcut the architecture thinking just because the feature is called Shortcut.
• Completely fair. And in plenty of cases, that IS the right answer. I mean, architecture is not religion — if a nightly copy gives you better performance, lower cost, and simpler security? Do that. Shortcuts shine when data governance says...
• Security model. External shortcuts use stored credentials — one credential, shared by everyone who accesses that shortcut. That's a fundamentally different security posture than internal shortcuts, where your own identity passes through....

Watch the episode

The Fabric Friday video on this topic: https://youtu.be/zjhbVF8NhWY

Resources

• OneLake Shortcuts
• Shortcut types
• Where to create shortcuts
• Shortcut authorization
• Shortcut caching
• Limitations
• Shortcut security
• Create OneLake Shortcut
• Create ADLS Gen2 Shortcut
• Create S3 Shortcut
• Create GCS Shortcut
• Shortcuts REST API
• On-premises Shortcuts
• Dataverse Shortcuts
• OneLake Overview

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.

Episode 1 • 2026-01-02 Duration: 8:32

Why the Fabric Lakehouse isn't a set-and-forget data platform. Matthias and Fabia unpack the real architectural tradeoffs — medallion layers, Delta table maintenance, Direct Lake, and when a Warehouse is actually the better call.

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

• That's the whole lesson. Don't architect for the demo. Architect for month six.
• Completely valid choice. If your team is SQL-first, no Spark needed, no ML — the Warehouse might genuinely be the better call. Pick the tool that matches the team and the workload, not the one that looks most impressive on the architecture slide.
• Fair. Not a trap — a tradeoff. You get Spark, flexibility, streaming and batch in one place. But you own the maintenance. That's the deal.

Resources

• Lakehouse end-to-end scenario
• Tutorial: Create a lakehouse
• Medallion architecture for Fabric
• Tutorial: Ingest data
• SQL analytics endpoint
• Direct Lake overview
• Delta Lake table optimization
• Decision guide: Lakehouse vs Warehouse
• High Concurrency Mode
• Lakehouse overview
• Table maintenance
• Work with Delta Lake tables
• Organize with Medallion architecture
• Use Apache Spark in Fabric
• Greenfield Lakehouse on 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.

Episode 0 • 2026-01-01 Duration: 3:41

Meet Matthias Falland and his AI co-host Fabia. Learn what this show is — anonymized real customer architecture decisions, cost realism, counter-arguments included — and what it is not. Weekly episodes, aligned with Fabric Friday recordings.

Key principles of the show

• Architecture is not religion.
• Pattern dictates platform.
• Simplicity on the slide is not simplicity at runtime.
• In a team of eight under cost pressure...

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.