Developing Event-driven Applications with Apache Kafka and Red Hat AMQ Streams
In this chapter, you learned:
Stateless transformations process every record of an event stream independently.
Stateful transformations process every record of an event stream by considering previous transformations, which are saved in a state store.
You can increase the processing rate of your topology by adding more threads or application instances.
In Kafka Streams, repartitioning entails moving the records of a stream to another topic. Kafka Streams performs repartitioning when the key of the records changes, and the records have to be grouped again in partitions based on the new key.
Welcome to the Developing Event-Driven Applications with Apache Kafka and Red Hat AMQ Streams group!
cschunke
26 wrz 2023
We are excited to launch a space dedicated to the Red Hat Training course Developing Event-Driven Applications with Apache Kafka and Red Hat AMQ Streams! To gain the most value from this group - click the "Join Group" button in the upper right hand corner of the group home page.We encourage group members to collaborate in this group to discuss topics, ask questions, share best practices and tips, provide course feedback, and share their accomplishments as it relates to AD482.Read more about Developing Event-Driven Applications with Apache Kafka and Red Hat AMQ Streams here.
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