In the previous post, I signed up for Databricks Free Edition and built a catalog, a schema, and a volume named dropzone. That volume is empty, though. Now it’s time to upload files to Databricks, and that’s where my two hats (Data Platform Architect and Power Apps MVP) actually have to talk to each other. My plan was simple: trigger a Power Automate flow from Dataverse, and have it upload a document straight into that volume. So I opened a…
I wear two hats. My profession is Data Platform Architect, where Databricks, Delta tables, and Unity Catalog are just another Tuesday. My hobby – if you can call it that – is being a Microsoft MVP for Power Apps, living inside Dataverse, Power Automate, and low-code. It is how it is. These two worlds rarely meet. This post is where I make them. If you’ve followed my spec-driven development series, you know I already extract receipts and invoices using Azure…
Baltic Summit 2026 just wrapped up in Gdynia, Poland, and I’m still working through my notes. Three days, a packed agenda, and honestly one of the best-organized conferences I’ve attended in a while. Registration was smooth, and the tracks were easy to follow. By the end, the whole event felt well thought out from start to finish. The overall theme across sessions was clear: AI, Copilot, Power Platform solutions, governance, and security. All of it was viewed from a dozen…
GitHub Copilot showed me something new: GPT-5.6 Sol, plus a 30% discount. Yes, and I couldn’t resist trying it on a Dataverse task. Why not see the new model in action, right? As a result, that decision burned nearly a month’s budget before the task was even done. But here’s the thing: the expensive part isn’t the story worth telling. What came out of it was a GitHub Copilot custom skill – a reusable file that captures everything the agent…
Missed my session at M365 Con DACH 2026? Good news – the recording is live. One note before you press play: it’s in German. On paper, my session covers Power Platform and document processing with AI Foundry Content Understanding. That’s the abstract I submitted. Reality had other plans. Shortly before the talk, my demo environment fell apart, and I had to rebuild it fast. So I turned to GitHub Copilot. It rebuilt the Dataverse tables, flows, and the Content Understanding…
The backlog from my last post had one line I couldn’t ignore: status transitions weren’t implemented yet. Tables, views, the model-driven app – all done. The missing piece was the Dataverse Agent Task contract itself. In other words, the part that lets Power Platform and Azure talk to each other. That wasn’t a bug. It was a missing seam. Without an explicit contract, two teams can’t build independently. They can’t test independently either. Remember my post about project decomposition? I…
“Dad, can we build an app with AI?” That question started a spec-driven development experiment I’ll never forget. He was seventeen. No coding background, no CS classes. Just frustration with the school carpool situation: thirty parents in a WhatsApp thread, nobody knowing who was driving, every morning a small disaster. I said yes. But on one condition: we use GitHub 🌱 SpecKit. He looked puzzled. Fair enough. So I explained: it’s a framework for Spec-Driven Development. Describe the problem the…
My spec was done. The plan too. Time to build the Dataverse foundation: the piece everything in my Expense Management system would run on. I connected the Dataverse MCP server in VS Code, opened my prepared spec, and ran /speckit.implement in GitHub Copilot. The GitHub 🌱 SpecKit workflow with Copilot to implement my Dataverse solution felt ready. I expected it to just work. It didn’t. The MCP hit its limits fast. Cryptic errors, wrong solution placement, no model-driven app support….
The Dataverse-first restart was done. However, my spec looked sharper. The data model had a center of gravity, and that part actually worked. Solving one problem, however, revealed another. One spec was still covering six very different concerns: different timelines, different risk profiles, and different people who would eventually build and own each piece. Nothing defined where one concern ended and another began. That’s not a spec problem. Instead, it’s a structure problem. In my previous post, I said the…
I ended my last post “Spec-Driven Development: When the Spec Is Too Big” with a promise: “My next post covers what happens when I restart with Dataverse-first.” Here it is. But Dataverse-first is harder to put into a spec than it sounds. I wanted AI to develop my optimal data model and not to dictate it upfront. The trick was giving GitHub 🌱 SpecKit the right bigger picture first. Describe the full problem. Steer toward the data layer. Then strengthen…