Every data team has been there: a metric spikes, a number drops, and no one noticed until it was too late. Manual monitoring doesn't scale — and waiting for someone to ask the right question in a meeting isn't a strategy.

In this session, I'll walk through how to build an automated anomaly detection pipeline entirely within Microsoft Fabric — from data landing in a Lakehouse, to statistical detection logic in PySpark notebooks, to anomalies surfaced as actionable signals in Power BI. You'll leave with a repeatable, end-to-end blueprint that catches unusual patterns automatically, before your stakeholders do.