Kafka Streams is a native streaming library for Apache Kafka that consumes messages from Kafka to perform operations like filtering a topic’s message and producing output back into Kafka. After working as a developer in stream processing, Bill Bejeck (Apache Kafka Committer and Integration Architect, Confluent) has found his calling in sharing knowledge and authoring his book, “Kafka Streams in Action.” As a Kafka Streams expert, Bill is also the author of the Kafka Streams 101 course on Confluent Developer, where he delves into what Kafka Streams is, how to use it, and how it works.

Building a large, stateful Kafka Streams application that tracks the state of each outgoing email is crucial to marketing automation tools like Mailchimp. Joining us today in this episode, Mitch Seymour, staff engineer at Mailchimp, shares how ksqlDB and Kafka Streams handle the company’s largest source of streaming data.

Availability in Kafka Streams is hard, especially in the face of any changes. Apache Kafka Committer and Kafka Streams developer Sophie Blee-Goldman shares about how to solve the stop-the-world rebalance and scaling out problem in Kafka Streams using probing rebalances.

Matthias J. Sax is back to discuss how event streaming has changed the game, making time management more simple yet efficient. He explains what watermarking is, the reasons behind why Kafka Streams doesn’t use them, and an alternative approach to watermarking informally called the “slack time approach.”

Apache Kafka® Committer Matthias J. Sax explains fault tolerance, high-availability stream processing, and Kafka Streams. He explains the differences between changelogging and checkpointing, as well as making changes in Kafka versus hot standbys, explaining what qualifies nodes as a hot standby, why Kafka Streams doesn’t do watermarking, and finally, why Kafka Streams is a library and not infrastructure.