Stream Processing, Kafka Streams, and Stateful Event Computation
Move beyond simple event transport and learn how Kafka supports transformations, joins, aggregations, and stateful stream processing.
Inside this chapter
- Why Stream Processing Matters
- Kafka Streams at a High Level
- Simple Transformation Example
- Stateful Processing
- Windowing
- Real-Time Example
Series navigation
Study the chapters in order for the clearest path from Kafka basics and local setup to stream processing, platform operations, cloud usage, and advanced event-driven architecture thinking. Use the navigation at the bottom to move smoothly through the full tutorial series.
Why Stream Processing Matters
Many systems do not just move events. They enrich them, aggregate them, join them with reference data, detect patterns, or derive new event streams. Stream processing turns event flow into real-time computation.
Kafka Streams at a High Level
Kafka Streams is a library for building stream-processing applications directly on Kafka topics. It supports filtering, mapping, aggregation, joins, windows, and local state stores.
Simple Transformation Example
KStream<String, OrderEvent> orders = builder.stream("orders");
KStream<String, PaymentEvent> payments = orders
.filter((key, value) -> "PAID".equals(value.status()))
.mapValues(order -> new PaymentEvent(order.id(), order.amount()));Stateful Processing
Stateful processing means the application remembers context over time. Examples include counting events per user, maintaining session state, deduplicating keys, or computing rolling aggregates.
Windowing
Windowing groups events by time boundaries such as tumbling, hopping, or session windows. This is common in analytics, monitoring, fraud detection, and engagement tracking.
Real-Time Example
A logistics company may aggregate courier location pings into per-minute route summaries and alert if no movement occurs for a threshold period. That is a stream-processing problem, not just a messaging problem.