Open Source Workflow Automation with Apache Kafka ft. Bernd Ruecker

The five components of latency are produce time, publish time, commit time, catch-up time, and fetch time. When consumer pulling adds to latency, Anna Povzner shares some best practices to keep in mind for how to think about partitioning in conjunction with latency. She also discusses client configuration in the cloud, interesting problems she's helped solve for customers, and her top two tips for debugging latency.

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.

Inventory management systems are crucial for reducing real-time inventory data drift, improving customer experience, and minimizing out-of-stock events. Kafka provides seamless inventory tracking at scale, saving billions of dollars in the supply chain, making modernized data architectures more important to retailers now more than ever. In this episode, we’ll discuss how Kafka enables stateful event streaming on a cloud-native platform for application and architecture modernization, leveraging Spring Boot, Kafka Streams, and Apache Cassandra.