Developing Event-driven Applications with Apache Kafka and Red Hat AMQ Streams
In this chapter, you learned:
Different notions of time exist in Kafka Streams: event time, processing time, and ingestion time.
Windowing operations allow developers to group events in time.
Kafka Streams can handle late events by using grace periods.
Idempotent Kafka producers prevent potential duplicates caused by retries.
Kafka consumers can deactivate the
autocommitfeature and use transactions to prevent data loss and duplication.Kafka Streams test utils provides methods to unit test topologies in isolation.
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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