Brendan Daly
Enterprise Data & AI | Strategy • Architecture • Governance
St. Louis, Missouri, United States
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As a Principal Consultant, I help organizations use data effectively, turning it into practical, reliable solutions that drive meaningful business outcomes. My experience covers designing scalable data platforms, integrating AI into real-world applications, and guiding strategic technology decisions that directly support business leaders.
I focus on creating clarity around complex problems and ensuring that technical work always connects to clear organizational goals.
If you’re navigating challenges around data architecture, modernizing systems, or integrating AI in ways that make a difference, let’s connect.
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Rethinking Embedded Analytics With Fabric Data Agents and Fabric Apps
Microsoft Fabric Data Agents are now generally available and consumable over an API, which means you can put a governed, natural-language analytics experience directly inside your own application. The question is no longer whether you can do it, but how to do it cleanly.
This is a hands-on, demo-driven session where we build the whole thing live. We'll start by creating and publishing a Fabric Data Agent, covering the best practices that make an agent actually reliable: data source selection, instructions, and validation. From there we'll set up the app registration that lets an application authenticate to the agent securely.
Finally, we'll embed the agent into a Fabric application built with Rayfin, the open-source SDK for building apps that run on Fabric, ending with a working conversational analytics experience inside a real app.
You won't leave with just concepts. We'll share a GitHub repo with repeatable labs and example patterns you can use to accelerate embedding agents into your own applications, so you can go from this session to a working build of your own.
Maps in Power BI: From Basic Geography to Production-Ready Spatial Analytics
Maps are one of the most requested, and most misused, visuals in the Microsoft data stack. Most reports lean on a default map without understanding the options behind it, the tradeoffs between them, or when a map actually adds analytical value.
This session walks through the map types available across Power BI and Fabric and what each one is built for, then digs into custom shapes and boundaries for sales territories, service areas, and districts. From there we'll look at where maps really earn their place: real-time data. We'll cover the new Fabric Maps item and how it pairs with Real-Time Intelligence, binding map layers directly to streaming data so they update as events arrive, and close with a live demo of a real-time mapping scenario built on RTI.
You'll leave knowing not just how to build maps, but which type fits which job, and how spatial context turns live operational data into something you can act on.
You'll learn:
- The map types available across Power BI and Fabric, and what each does well
- How to work with custom shapes and boundaries for territories and regions
- Why maps are a natural fit for real-time and streaming data
- What the new Fabric Maps item adds in Real-Time Intelligence
- Best practices for performance, privacy, and governance for GIS in reporting
This pairs well with the geospatial data engineering session I also submitted
Location as a Universal Foreign Key: Spatial Analytics in Microsoft Fabric
Customers have addresses. Sites and sensors have coordinates. Orders ship somewhere. Even data with no location of its own usually belongs to a region or territory that has one. Location is a key that can join datasets that were never designed to join, and most analytics treat it as a label instead of a relationship.
Taking that relationship seriously starts with how location is represented: points, lines, and polygons, and the coordinate system that gives them meaning. From there, a small set of spatial relationships does most of the work. What is near what? What falls within what? What overlaps?
This session walks through that progression inside Microsoft Fabric. We'll see how spatial data sits alongside business data in a lakehouse, how those relationships can be expressed with SQL spatial functions and Python with GeoPandas, and how the results flow into reporting like any other table.
Along the way we'll cover the ideas that trip up newcomers, including why a coordinate system choice can quietly break a distance calculation.
By the end, you'll read your existing data differently, and you'll know what questions are now open to you.
The Cloud & AI Summit 2026 Sessionize Event Upcoming
Saint Louis Microsoft Fabric/Power BI Meetup Group User group Sessionize Event
Brendan Daly
Enterprise Data & AI | Strategy • Architecture • Governance
St. Louis, Missouri, United States
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