Materialized Lake Views in Microsoft Fabric: How They Actually Work

Video by: Reid Havens

Every report hitting your raw tables runs the same expensive joins and aggregations over and over, even when the underlying data hasn't moved. Materialized Lake Views in Microsoft Fabric pre-compute those results once and store them as Delta tables in OneLake, so consumers read finished numbers instead of recalculating from scratch.

In this video I cover what MLVs actually are, how the automatic refresh logic decides between skip, incremental, and full rebuild, the Spark SQL syntax to create and manage them, and where they sit in a medallion architecture (think of them as a "Gold+" layer).

The part worth sticking around for: point Direct Lake at an MLV instead of your raw tables and you get pre-aggregated data at import speed. In the example here, a 50 million row fact table collapses to around 500K. Fewer rows means faster transcoding into VertiPaq and a lot less DirectQuery fallback risk. That's the single best reason to pair the two.

I also get into when NOT to use them, because they're not free. Sub-minute volatile data, tiny tables, and row-level security needs are all cases where an MLV is the wrong tool.

Curiosity, Creativity, and Chemistry: How I Stopped Copiloting and Started Collaborating - with James Bartlett

LIVESTREAM DATE/TIME 📅

June 11th 2026 - 9:30 AM (Pacific Time)

DESCRIPTION 📄

Most people use AI assistants like fancy search engines, and the results often leave much to be desired. But James Bartlett isn't most people. He has a personal AI agent named Cray, whom he treats more like a junior colleague, and they have racked up some interesting and impressive achievements together. Cray runs 24/7 on James's home server, managing scheduled jobs, helping James with otherwise boring and repetitive tasks, and building dozens of tools (including an entire PowerShell MCP framework) during unsupervised nightly "innovation sessions."

In this conversation with Reid Havens, James pulls back the curtain on what daily life with an AI partner actually looks like: the good, the weird, the genuinely useful, the unexpectedly wholesome, and the parts that honestly still need some work. If you've wondered what's beyond "prompt engineering," this is that conversation.

GUEST BIO (James Bartlett) 👤

James D. Bartlett III is a Microsoft Data Platform MVP, co-host of The Drill Down with Ahmad & James podcast, and an unapologetic PowerShell enthusiast. By day, he helps organizations wrangle Microsoft Fabric and Power BI at P3 Adaptive, and by night, he tinkers with open-source software projects, home automation, vintage audio gear, local LLMs, and an AI agent named Cray who lives on his LAN and builds handy tools while he sleeps.

Introducing the NEW Tabular Editor CLI - with Peer Grønnerup

LIVESTREAM DATE/TIME 📅

May 29th 9:30 AM (Pacific Time)

DESCRIPTION 📄

Command line tools have a reputation problem. People hear "CLI" and either tune out or assume they need to be a DevOps engineer to get any value out of it. For most semantic model developers, that's been the story for years.

The new standalone Tabular Editor CLI changes that. You don't need to live in the terminal to automate validation, deployment, and testing across your tabular models. You just need a starting point.

In this livestream Peer will be walking through the new CLI from scratch: what it is, why it matters for Power BI and Fabric work, and how it fits into the rest of your workflow (AI agents included, if that's your flavor). We'll cover:

• What the Tabular Editor CLI actually does

• Local setup with zero prior CLI experience

• Using AI agents to generate and run commands for you

• Automating model validation, testing, and deployment

• Wiring it into CI/CD without breaking everything No prior command line experience required.

If you've been CLI-curious but didn't know where to start, this one's for you.

GUEST BIO (Peer Grønnerup) 👤

Peer is a Data & AI professional with 15+ years of experience in BI, semantic modeling, and data platform automation. As Head of Engineering at Tabular Editor, he focuses on building tools and features that help teams create better data models faster, with a strong emphasis on automation, developer experience, and CI/CD, including leading the development of the new Tabular Editor CLI.

RELATED CONTENT 🔗

Peer's Blog
Peer's GitHub
TE CLI Release

Understand DAX Context in Power BI: CALCULATE, Filter Context & Row Context (Part 2)

Video by: Reid Havens

The single biggest concept separating DAX beginners from confident report builders is context. This interactive guide breaks down how Power BI evaluates every formula you write: what filter context is, how CALCULATE manipulates it, the difference between row context and filter context, how context transition bridges the two, and why iterator functions like SUMX create their own row context. If CALCULATE has ever confused you, start here.

Topics covered:

• The mental model shift: why DAX doesn't think like Excel

• Query context: how rows and columns shape evaluation

• Filter context: slicers, relationships, and implicit filters

• CALCULATE: overriding, replacing, and adding filter arguments

• Row context: calculated columns, iterators, and the current row

• Context transition: what happens when CALCULATE meets row context

• Iterator functions: SUMX, AVERAGEX, and row-by-row evaluation

RELATED CONTENT 🔗

DAX Context (Part 2)
Analytic Endeavors