Siddharth Bedekar

Confluent Managed solution for Apache Flink is expanding its analytical capabilities with the introduction of ML_FORECAST and ML_ANOMALY_DETECTION functions. Developers can now harness the power of established models like ARIMA for continuous forecasting and anomaly detection, all within the familiar SQL interface. This advancement eliminates the need for external ML services and enables continuous processing by embedding these analytical capabilities directly in your streaming pipeline. In this 20-minute session, tailored for developers with stream processing experience, we'll explore how to integrate sophisticated time series analysis into Flink SQL applications. We'll start by introducing the newly developed ML_FORECAST function, which brings ARIMA modeling capabilities to streaming data. We'll then demonstrate the ML_ANOMALY_DETECTION function and show how it can be combined with Kafka-sourced data streams for real-time anomaly detection. Finally, we'll build a complete streaming application that combines both functions to forecast metrics and detect anomalies in a continuous manner. By the end of the session, attendees will understand how to leverage these powerful new…

Confluent Managed solution for Apache Flink is expanding its analytical capabilities with the introduction of ML_FORECAST and ML_ANOMALY_DETECTION functions. Developers can now harness the power of established models like ARIMA for continuous forecasting and anomaly detection, all within the familiar SQL interface. This advancement eliminates the need for external ML services and enables continuous processing by embedding these analytical capabilities directly in your streaming pipeline. In this 20-minute session, tailored for developers with stream processing experience, we'll explore how to integrate sophisticated time series analysis into Flink SQL applications. We'll start by introducing the newly developed ML_FORECAST function, which brings ARIMA modeling capabilities to streaming data. We'll then demonstrate the ML_ANOMALY_DETECTION function and show how it can be combined with Kafka-sourced data streams for real-time anomaly detection. Finally, we'll build a complete streaming application that combines both functions to forecast metrics and detect anomalies in a continuous manner. By the end of the session, attendees will understand how to leverage these powerful new…