Streams and Tables in Apache Kafka: Event Processing Fundamentals

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This post details the fundamental concepts of event processing in Apache Kafka, focusing on streams and tables as processing layer concepts distinct from Kafka's storage layer (topics, partitions, brokers). It explains how events in Kafka topics are transformed into streams (unbounded sequences with schemas) and tables (aggregated streams, often materialized views). The post also covers data contracts and schema management, emphasizing schema-on-read and the benefits of schema-on-write with Confluent Schema Registry. Finally, it delves into the partitioning of processing, explaining consumer groups and stream tasks as the units of parallelism for distributed applications, and the Kafka rebalance protocol for automatic workload redistribution.