As businesses move toward hyper-responsive operations, the ability to detect anomalies and act on time series data in real time is becoming mission-critical. This session dives deep into how Microsoft Fabric’s Real-Time Intelligence (RTI) stack—powered by Kusto Query Language (KQL)—enables streaming time series analytics and intelligent anomaly detection at scale.

You’ll explore advanced KQL capabilities to:

Build and analyze dynamic time series models over streaming data using make-series, summarize, and render timechart

Apply native anomaly detection algorithms (series_decompose_anomalies, series_outliers, etc.) in real-time

Detect trend shifts, spikes, volatility, and data absence (heartbeat failure) in high-cardinality signals

Combine stateful pattern detection in KQL with event routing via Activator for intelligent automation

We’ll walk through real-world use cases—like monitoring IoT telemetry, infrastructure stability, and SLA breaches—and showcase how to embed anomaly detection directly into streaming dataflows with sub-minute latency using Eventstreams, KQL, and Activator.

Expect hands-on demos, deep syntax insights, and performance tuning tips for advanced users. Whether you're building operational alerting for manufacturing or real-time analytics for finance, this session will help you take full control of your time series data in Fabric.