An Apache Flink and Kotlin streaming architecture where online machine learning detects concept drift from machinery wear, controlled by a shadow mode router.

Examples from the book Stream Processing with Apache Flink ported to Kotlin, updated to current Flink APIs and built with Gradle after PyFlink lacked features.

Flink Table API in Kotlin states the supplier statistics as a declarative windowed aggregation over a DataStream, with late rows routed by hand.

Flink DataStream API in Kotlin computes the same supplier statistics, using watermarks for event time and side outputs to collect late order events.

Kafka Streams in Kotlin aggregates Avro order events into tumbling window supplier statistics and handles late records with a custom extractor.

Avro and Schema Registry replace hand written JSON codecs in a Kotlin Kafka producer and consumer, with generated classes and graceful shutdown.

A Kotlin Kafka producer and consumer for order events, with custom JSON serialisers, admin helpers and a Gradle build, run on Factor House Local.