Joins & Aggregations in Streams

In real-world data processing, you often need to combine information from different sources. Imagine tracking user clicks and matching them with user profiles, or correlating an order with its payment details.

Kafka Streams provides powerful operations to join different data streams (KStreams) and tables (KTables) based on a common key. This allows you to enrich your data and derive more complete insights in real-time.

A KStream-KStream join combines records from two KStreams based on their shared key. Since KStreams represent unbounded, continuous event streams, these joins require a time window.

• Events from both streams must arrive within this defined time window to be considered for a join.
• If an event from one stream arrives outside the window of its matching event in the other stream, they won't be joined.
• This is crucial for correlating events that happen close together, like a user clicking an ad and then visiting a product page.