Kafka Streams in Action | Confluent

What this post added

This post introduces Kafka Streams as a library for building event-driven applications in Java to process data in Apache Kafka topics. It highlights how applications using this library can perform sophisticated transformations on data streams, with fault tolerance and elastic distribution across application instances. The post contrasts Kafka's stream processing approach with Big Data frameworks like Storm, Flink, and Spark, emphasizing Kafka's goal of making stream processing accessible for event-driven applications rather than a heavy-weight 'big data' task. It also discusses the broader trend of event-driven architectures powered by Apache Kafka and the shift towards continuous, real-time processing of business events.