Detecting the Unexpected: Built-in Real-Time Anomaly Detection in Confluent Cloud for Apache Flink®

What this post added

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.