Writing
Notes on software engineering, data platforms, AI, and the craft of building things.
An ontology is a written map of the things your business runs on and how they connect. Why you need one the day your answers depend on connections across systems, what the building blocks are, which tools build it, and how to build one in-house.
Copilot and Fabric Data Agents answer from your semantic model's metadata, so preparing the model is the real AI work. A persona-mapped, flowchart-driven guide with brownfield and greenfield checklists.
A Power BI semantic model in Microsoft Fabric can be tuned for self-service exploration, certified analytics, or AI agents, but not all three at once. Six dimensions pull the model in different directions, and the one-source-three-models pattern resolves the conflict.
AI knowledge retrieval works best as a router: match each question type (lookup, meaning, connect-the-dots, whole-collection) to the right search method (keyword, hybrid, reranking, multi-step, graph) instead of defaulting to vector search for everything.
Beginner-friendly guide to choosing hash functions across Microsoft Fabric. Why Spark hash() breaks at scale, how to make SHA-256 match across Spark, Warehouse, and KQL, and a top-5 comparison on F64 SKU.
Five levers control all Delta table performance in Microsoft Fabric: resource profiles, V-Order, OPTIMIZE and Liquid Clustering, default behaviors, and VACUUM. A decision framework for data engineers and Fabric architects working with Lakehouses at scale.
A structured rubric for assessing Microsoft Fabric operational maturity. Six domains, five levels, and an interactive dashboard to score your deployment and surface prioritized gaps.
Complete coverage of 30+ announcements from the combined FabCon + SQLCon event: Fabric IQ, OneLake Security, Runtime 2.0, Database Hub, and more.