There you are, the data streaming engineer. You’ve architected an event-driven application ecosystem - a land where microservices produce events to Apache KafkaⓇ, change data capture (CDC) socializes transactions to Kafka topics, and connectors bring data from external systems into this streaming universe.

Now you need to process these streams to create insightful data products for your organization. In a JVM shop, you’ve read the tales of Kafka Streams and implemented “word count” examples galore. That’s great, but there’s so much more.

Let’s dive into the declarative Kafka Streams DSL and explore the operators that turn simple applications into sophisticated stream processing engines. We'll talk stateful transformations, zooming in on joins, aggregations, and windowing - why they are essential for building real-time data pipelines.

Slides are good, code is better. So let’s look at code - both the implementations and how to unit test our stream processing applications.

Join me to level up your skills with the Kafka Streams DSL to bring insights from your data streams.