Building to Teach: AI-Assisted Tools for Data Viz Education (w/ Ben Ferry)

LIVESTREAM DATE/TIME 📅

September 4th, 2026 - 9:30 AM (Pacific Time)

DESCRIPTION 📄

What if you could use your own expertise and AI to build a tool that didn't exist yet? Ben did exactly that.

He built Ferry Tales Studio, a browser-based Vega-Lite chart editor for Power BI and Deneb users, using Claude Code to design, iterate, and ship something genuinely useful. In this live demo, Ben walks through the tool and explains why he built it: to give people a hands-on way to learn data visualisation by doing.

The takeaway: you don't need to be a software engineer to build tools that teach.

GUEST BIO (Ben Ferry) 👤

Ben Ferry is a Senior Analytics Consultant at Solita Germany with a passion for data visualisation and making it more accessible. Through Power BI Ferry Tales, he creates content and tools to help the data community grow. Ferry Tales Studio, built with Claude Code, is his latest example of how practitioners can use AI to ship real, useful products without a traditional engineering background.

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Building Custom Data Experiences with Power BI and Fabric Apps (w/ Stephen Godderidge)

LIVESTREAM DATE/TIME 📅

August 21st 2026 - 9:30 AM (Pacific Time)

DESCRIPTION 📄

Power BI reports are great for helping people understand their data, but some scenarios call for more flexibility, customization, and interactivity than a traditional report can provide.

Fortunately, Fabric Apps gives developers a new way to build tailored data experiences on top of Microsoft Fabric and Power BI semantic models.

We'll cover:

• Where Fabric Apps fit alongside Power BI reports

• When a custom data app makes sense

• How these experiences can help users move from insight to action

If you build on Power BI semantic models and have run into the limits of what a report can do, this one is worth your time.

GUEST BIO (Stephen Godderidge) 👤

Stephen Godderidge is a Principal Product Manager for Power BI within Microsoft Fabric, where he focuses on developer integrations, data apps built with Fabric Apps, and the Model Context Protocol (MCP). His experience spans consulting, solution architecture, and product development, giving him a practical perspective on building scalable, developer-friendly data solutions. He is passionate about sharing what he learns and learning alongside the Microsoft data community.

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BI Pixie Workload on Fabric: From Power BI Usage to AI Readiness - w/ Gil Raviv

LIVESTREAM DATE/TIME 📅

August 7th 2026 - 9:30 (Pacific Time)

DESCRIPTION 📄

Power BI tells you how many times a report was opened, but not whether people actually understood it, trusted it, or found what they needed. BI Pixie is a usage analytics and AI readiness solution for Power BI, now available as a native Microsoft Fabric workload.

In this session, we'll demo the latest BI Pixie features and take you on a journey from Power BI usage to AI readiness. We'll start by showing how BI Pixie captures what really happens inside your reports, with visual-level heatmaps, click and filter tracking, and in-report satisfaction signals that reveal which content drives value and which quietly gets ignored.

Then we'll move up the stack to the part everyone is asking about: getting your data ready for AI. You'll see how BI Pixie assesses whether your semantic models are ready for AI agents like Copilot, Fabric data agents, and MCP with an engagement-weighted Readiness Score; how it optimizes those models with targeted improvements; and how you can create benchmarks to verify the effectiveness of your data agents and semantic models over time.

If you own Power BI adoption and you're wondering how to prepare your estate for AI, this one is for you.

GUEST BIO (Gil Raviv) 👤

Gil Raviv is the founder of BI Pixie and DataChant Consulting, and a Microsoft MVP. Gil has led global BI practices at Avanade/Accenture and AWS and today focuses on helping enterprises measure and improve the adoption, trust, and governance of their Power BI reports through BI Pixie.

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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.