Short demo of how to do joins in Kafka Streams

A word from the author:
"Working with event streams is essential because they allow you to respond to events as they happen. As the native stream processing library for Apache Kafka, Kafka Streams provides just that ability. With stateless and stateful operations like map, flatMap, filter, groupBy, reduce, and aggregate, there's a lot of power at the developers' fingertips to build a powerful event streaming application.

But sometimes, you want to enrich an event stream with additional information. For example, consider a stream of purchases where the only customer information is the customer id. If you wanted to take further action downstream, it would be essential to add some additional context concerning the customer. Kafka Streams provides the ability to do something like this with joins. Kafka Streams supports stream-stream joins and stream-table joins, which joins a KStream with either a KTable or GlobalKTable."

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About the book:
Event Streaming with Kafka Streams and ksqlDB teaches you to implement stream processing within the Kafka platform. In this easy-to-follow book, you’ll explore real-world examples to collect, transform, and aggregate data, work with multiple processors, and handle real-time events. You’ll also dive into processing event data with ksqlDB. Practical to the very end, it finishes with testing and operational aspects, such as monitoring, debugging, and gives you the opportunity to explore a few end-to-end projects.