Building an AI-Driven Ops Command Center with Power BI Over the last few years, I have been working on a practical framework to move operations teams from reactive monitoring to AI-augmented operations. This article shares a simple, real-world approach to building a governed Ops & Reliability Analytics platform using Power BI – something SREs, DBAs, and platform teams can actually use daily. 1. Architecture First: Don’t Just Connect, Model Properly: One common mistake I see is connecting Power BI directly to raw telemetry tables and expecting magic. With millions of rows, reports quickly become slow and confusing. The solution is a Unified Star Schema. You should centralise core dimensions like Date, Asset, Service, and Database. On the fact side, bring in telemetry (5‑minute or hourly), incidents, change records, and ML-based risk scores. Keep relationships simple and use single‑direction filters from dimensions to facts. Avoid bi‑directional filters unless absol...
The first time I heard people talking seriously about DLT in Databricks, I noticed something interesting. The room was full of smart people, but everyone seemed to mean slightly different things when they said “DLT.” Some people were talking about automation. Some were talking about data quality. Some were treating it like just another pipeline feature. And people coming from Oracle or traditional ETL backgrounds often had the same silent question: “Fine, but what is it really? And how is it different from the way we already build pipelines?” I could relate to that question immediately. Because if you have spent years around Oracle, PL/SQL, scheduler jobs, ETL tools, control tables, recovery scripts, and operational dashboards, then modern cloud data platform language can sometimes sound more complicated than it needs to be. In the older world, even when things were complex, the mental model was clear. You had jobs. You had dependencies. You had scheduling. You had ...
A few years ago, building a data platform felt like managing a crowded marketplace. There was a data lake sitting quietly in object storage. A warehouse lived somewhere else for dashboards. ETL pipelines ran in their own tool. Streaming had another engine. Machine learning experiments happened in separate notebooks. Each team had its space. Each system did its job. But they didn’t naturally work together. Now picture a fast-growing retail company expanding across cities. Sales data flows in daily. Engineers load raw files into cloud storage. Analysts copy pieces into a warehouse for reports. Data scientists request extracts to build models. Meanwhile, governance teams try to answer simple questions like, “Who accessed this table?” The answers aren’t always clear. Nothing is completely broken. But everything feels stitched together. Databricks entered this story with a different idea. Instead of improving the stitching, it asked: What if the lake itself could act like a wareh...