Preventing and Fixing Bad Data in Event Streams, Part 2

What this post added

This post details strategies for handling bad data in event streams, focusing on prevention through schemas and tests, and remediation through event design and data recovery. It introduces the concept of State vs. Delta events, highlighting how State events, when combined with Kafka's topic compaction, enable effective deletion and correction of bad data. The post provides examples of fixing bad data at the source using Kafka Connect with Debezium for database changes, and within Kafka Streams applications, emphasizing the use of Dead Letter Queues (DLQs) for unrecoverable events.