Detecting the Unexpected: Built-in Real-Time Anomaly Detection in Confluent Cloud for Apache Flink®
Nov 13, 2025Mayank Juneja
Summary
Introduces built-in anomaly detection capabilities in Confluent Cloud for Apache Flink using the `ML_DETECT_ANOMALIES` function, which leverages ARIMA models. This function supports auto-ARIMA parameter determination, seasonal pattern detection via STL, adaptive training windows (`minTrainingSize`, `maxTrainingSize`, `updateInterval`), and confidence-based detection (`confidencePercentage`). The post details real-world use cases and provides an example SQL query for its implementation.
More from the Real-time Enterprise RAG trail
- Building an Agentic SOC on a Stream
- Hemut: Building the Internet of Freight on Real-Time Data
- New in Confluent Intelligence and AI Tools: Making Agents Native to the Stream, Expanded Model Support, New Agent Skills, and Copilot
- Customer Intelligence Hub for Real-Time GTM Insights
- RAG and GenAI for Customer 360 Architecture