Java

Table of Contents Introduction Why Stream Processing? A Quick Primer Kafka Streams Architecture Overview Core Concepts 4.1 KStream vs. KTable vs. GlobalKTable 4.2 Topology Building Stateful Operations 5.1 Windowing 5.2 Aggregations & Joins Exactly‑Once Semantics (EOS) Fault Tolerance & State Management Testing & Debugging Kafka Streams Applications Deployment Strategies Performance Tuning Tips Real‑World Use Cases 12 Best Practices & Common Pitfalls Conclusion Resources Introduction Apache Kafka has become the de‑facto backbone for event‑driven architectures, but many teams struggle to extract real‑time insights from the raw event flow. That’s where Kafka Streams steps in: a lightweight, client‑side library that lets you write stateful stream processing applications in Java (or Kotlin) without managing a separate processing cluster. ...