Working with unbounded and fast-moving data streams has historically been difficult. But with
Kafka Streams and ksqlDB building stream processing applications is easy and fun. This
practical guide shows data engineers how to use these tools to build highly scalable stream
processing applications for moving enriching and transforming large amounts of data in real
time. Mitch Seymour data services engineer at Mailchimp explains important stream processing
concepts against a backdrop of several interesting business problems. You'll learn the
strengths of both Kafka Streams and ksqlDB to help you choose the best tool for each unique
stream processing project. Non-Java developers will find the ksqlDB path to be an especially
gentle introduction to stream processing. Learn the basics of Kafka and the pub sub
communication pattern Build stateless and stateful stream processing applications using Kafka
Streams and ksqlDB Perform advanced stateful operations including windowed joins and
aggregations Understand how stateful processing works under the hood Learn about ksqlDB's data
integration features powered by Kafka Connect Work with different types of collections in
ksqlDB and perform push and pull queries Deploy your Kafka Streams and ksqlDB applications to
production
