Advanced Timeseries Analytics and Anomaly detection in Fabric Realtime Intelligence

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.

Understand how Microsoft Fabric Real-Time Intelligence and KQL support real-time time series analytics at scale Build and analyze streaming time series models using advanced KQL techniques Apply native KQL anomaly detection functions to identify spikes, trends, outliers, and missing data Implement stateful pattern detection and event-driven actions using Activator Design low-latency, real-time anomaly detection solutions for scenarios such as IoT, infrastructure monitoring, and SLA tracking