(This is the second post in a three-part series on how AI is changing the way we build solutions in Microsoft Fabric. The first post explored the new unified Copilot in Fabric and Project Osmos help build and transform data, … Continue reading →
(This post starts a three-part series on how AI is changing the way we build solutions in Microsoft Fabric. First, we’ll explore how the new unified Copilot in Fabric and Project Osmos help build and transform data, moving from AI … Continue reading →
If you are migrating a data warehouse, there is a question worth asking before you start: should you recreate the existing solution on the new platform, or use the move to make it better? This applies whether you are moving … Continue reading →
Microsoft shared a broad set of Fabric, Power BI, and SQL updates at FabCon and SQLCon Barcelona 2026, with approximately 5,000 attendees at the conference. I’ve tried to make this a fairly exhaustive roundup of the major announcements and relevant … Continue reading →
Making Data AI-Ready, Part 3 (This is the final article in a three-part series on making data AI-ready. Part 1 covered what AI-ready data means, Part 2 looked at the Microsoft tools that support it, and this article focuses on … Continue reading →
Making Data AI-Ready, Part 2 (This is the second article in a three-part series on making data AI-ready. Part 1 covered the fundamentals of AI-ready data, this article looks at the Microsoft tools that support it, and Part 3 explains … Continue reading →
Making Data AI-Ready, Part 1 (This is the first article in a three-part series on making data AI-ready. Part 1 explains what AI-ready data actually means, Part 2 looks at the Microsoft tools that help you get there, and Part … Continue reading →
Most enterprise questions do not live neatly in one place. The numbers may be in a warehouse, the rules may be buried in PDF files, and the explanation may sit in a support document that only three people know exists. … Continue reading →
Microsoft Fabric makes it wonderfully easy to put many analytics workloads on one platform. Power BI, data engineering, warehousing, data science, real-time analytics, Copilot, and other experiences can all share the same Fabric capacity. That is a big advantage, but … Continue reading →
An architecture scenario Imagine separate Sales, Finance, Shared Data, and Executive Analytics workspaces. Sales owns sales transactions, Finance owns budgets, and Shared Data owns common tables such as Customer, Product, and Date. Executive Analytics needs selected data from all three, … Continue reading →
Originally published April 2022; substantially updated July 2026. This post is a complete replacement for and update to my April 2022 post, Power BI Performance Features. That post focused on several performance features that were new or in preview at … Continue reading →
It can feel as if Microsoft has put the Copilot name everywhere, and that is not far from the truth. The important thing to understand is that Microsoft Copilot is not one identical AI assistant copied into every product. It … Continue reading →
The idea behind Fabric OneLake Security (which GA’d on April 2026) is to centralize data access controls at the data layer, rather than configuring security separately for every Fabric experience. You define security once, close to the data in OneLake, … Continue reading →
Once again there were a number of Microsoft Build announcements related to data and AI, and some were very impressive. Below are my favorites. I am prioritizing the data announcements first, because that is where my brain naturally goes (and … Continue reading →
Microsoft Purview can be the best data governance tool in the world, but it will still be useless if people do not know it exists, do not trust the metadata, or do not change the way they work. That is … Continue reading →
Since the release of my book Deciphering Data Architectures: Choosing Between a Modern Data Warehouse, Data Fabric, Data Lakehouse, and Data Mesh, I’ve been fortunate to hear from readers around the world. One question I’ve received frequently is whether the … Continue reading →
What problem is Fabric Ontology trying to solve? For years, most data conversations have started with tables. We ask where the data lives, what columns are available, how the joins work, and whether the data is in a warehouse, lakehouse, … Continue reading →
Model Context Protocol, or MCP, is one of those technical ideas that sounds more complicated than it really is. The easiest way to think about it is this: MCP is to AI what USB was to hardware. Before USB became … Continue reading →
There’s a question I’ve been hearing more and more lately, especially as Copilot, Fabric, and Fabric data agents become part of everyday conversations with customers: will GenAI replace reports and dashboards? It’s a fair question, because on the surface they … Continue reading →
Once your Fabric tenant grows past a few workspaces, data discovery gets weird fast. Duplicates multiply. People stop trusting what they find. And security teams start hearing things like, “I think only the right people have access” (which is never … Continue reading →