Writing
Notes on software engineering, data platforms, AI, and the craft of building things.
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.
A phased, practical framework for making your Fabric semantic models AI-ready: metadata enrichment, Prep for AI configuration, Data Agent validation, and multi-surface deployment.