Stream Processing with KStream & KTable
Learn to use KStream for immutable event streams and KTable for stateful, updatable views of data, performing operations like filtering and mapping.
KStream & KTable Unveiled
Welcome! In Kafka Streams, KStream and KTable are your primary tools for processing data. They represent different views of your data in motion.
Think of them as two sides of the same coin, each suited for distinct stream processing tasks. Understanding their differences is key to building powerful stream applications.
KStream: Immutable Events
A KStream represents an infinite, immutable sequence of events. Each record in a KStream is a self-contained fact, an independent event that happened at a specific point in time.
- It's like a transaction log: once an event is added, it's never changed.
- Operations on a KStream produce new KStreams, leaving the original untouched.
- It's ideal for processing individual events like clicks, sensor readings, or log entries.
All lessons in this course
- Introduction to Kafka Streams
- Stream Processing with KStream & KTable
- Building a Simple Stream Application
- Windowing and Stateful Aggregations in Kafka Streams