Dataflow Programming: Building a Java Pipeline With Flink and Kafka

This post details building a Java pipeline with Apache Flink and Kafka to process clickstream data. It covers setting up a Maven project with Flink dependencies, creating a Flink uber JAR, defining POJOs for data serialization (ClickStreamRecord and ClickStreamAnalytics), creating a Flink entry point, and configuring the stream execution environment. The pipeline consumes JSON records from a Kafka topic, deserializes them, groups records by key and time window (1 minute), and computes success/failure counts for each request type, producing the results to a separate Kafka topic. It highlights the use of Flink's POJO serializer and the importance of efficient data structures for performance.