Data Ninja AI Lab

Building practical data systems with Microsoft Fabric, Power BI, and AI.

I use this lab to publish practical articles, demos, and code around Microsoft Fabric, Power BI, analytics engineering, automation, and AI systems.

My goal is simple: turn real implementation work into clear patterns other data professionals can use. Less theory, more decisions, tradeoffs, architecture, and working examples from the messy middle of building reliable data platforms.

Latest articles

Power BI Maps Can Finally Follow the Analysis

A four-case acceptance test for filtered-selection reloads and automatic zoom in Azure Maps, including context, viewport, totals, and the 30,000-point boundary.

Give Your Fabric AI Agent a Real Dependency Map

A read-only review workflow for the Fabric item relations API preview, with typed dependencies, evidence boundaries, and acceptance tests before a person approves a change.

Let Your Fabric Gateway Scale With the Workload

A practical operating model for VNet data gateway autoscaling: workload inventory, demand baselines, scale limits, cost guardrails, and ownership before private data access becomes a refresh incident.

Make Fabric AI Agents Smarter With Labels You Already Own

A practical guide to using Fabric sensitivity labels as context signals for AI agents, with a label-to-behavior map, pilot playbook, answer-quality checklist, and governance rules.