Reading events from a queue is the easy part. The harder part is turning those events into reliable output when data arrives late, arrives out of order, or a machine fails halfway through processing.

Streaming engines are built for this. They process events as they arrive by filtering, joining, enriching, aggregating, and routing them. They also handle the reliability work: event time, state, checkpoints, replay, backpressure, and safe writes to downstream systems.

This chapter explains how streaming engines work, how Apache Flink, Kafka Streams, and Spark Structured Streaming differ, how state, watermarks, checkpoints, and output writes fit together, and how to choose the right engine for production.