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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Simplify Power BI DAX with Visual Calculations

Video by: Reid Havens

If you've ever written a running total in DAX, you know it's more work than it has any right to be. CALCULATE, FILTER, a column reference you have to get exactly right, and a quiet prayer that it behaves at the subtotal level. Visual calculations let you write RUNNINGSUM([Sales]) and get on with your day.

They're a calculation layer that lives inside the visual itself, working on the data that's already been aggregated rather than querying the whole model. That's also why they're often faster, sometimes dramatically so on DirectQuery.

I walk through the core functions (running totals, moving averages, PREVIOUS and NEXT for period comparisons, FIRST, LAST, INDEX, and RANK), then the hierarchy functions like COLLAPSE and EXPAND that make "% of parent" and "% of grand total" almost trivial. We also get into AXIS and RESET, which control the direction a calculation runs and where it restarts, because that's the part that quietly breaks people's running totals across years.

One honest caveat. Yes, they're easier and faster. But because each one only lives in a single visual, they can fragment your business logic across reports if you're not careful, so I cover when a visual calc is the right call and when it really should be a centralized measure.

DAX Fundamentals Part 3: The Patterns That Make Reports Work

You've learned how filter context and CALCULATE actually work. This is where it pays off. Part 3 of the DAX Fundamentals series covers the patterns you'll keep reaching for in real models, and why they behave the way they do.

We get into time intelligence (YTD, QTD, prior cycle, and why these functions return tables of dates rather than numbers), variables for cleaner and faster formulas, and SWITCH as the cure for nested IF chains nobody wants to maintain. Then inactive relationships and USERELATIONSHIP for when one fact table has two date columns fighting over the same date table.

The pattern that saves the most pain is calculation groups. Write three base measures, add one calc group with YTD, Prior Cycle, and YoY%, and you've replaced nine measures (and growing) with something you maintain in one place.

The video closes on the part most people skip: how the engine splits work between the Storage Engine and the Formula Engine, and why pushing work toward simple aggregations on a star schema is the difference between fast DAX and slow DAX.