During his time at Twitter, Sam Ritchie led the development of Summingbird, a project that helped Twitter ingest and process massive amounts of data, relieving some key pain points for developers at Twitter. In this episode, Sam dives teaches us some abstract algebra and explains how it has informed his attempts to make stream processing programs easy to write in a more general way.

Apache Kafka® 2.5 is here, and we’ve got some Kafka Improvement Proposals (KIPs) to discuss! Tim Berglund shares improvements and changes to over 10 KIPs all within the realm of Core Kafka, Kafka Connect, and Kafka Streams, including foundational improvements to exactly-once semantics, the ability to track a connector’s active topics, and adding a new co-group operator to the Streams DSL.

James Urquhart (Global Field CTO, VMware) is writing a book about worldly mapping and evaluating user needs in order to make event streaming a more economic choice for users. James argues that reducing the cost of integration does not deter people from buying but instead encourages creativity to find more uses for integration.

There’s something about YAML and the word “Docker” that doesn’t sit well with Viktor Gamov (Developer Advocate, Confluent), but Kafka Streams on Kubernetes is a phrase that does. Viktor describes what that process looks like and how Jib helps build, test, and deploy Kafka Streams applications on Kubernetes for an improved DevOps experience.

Coming from decades of experience in messaging, Dan Rosanova discusses the pros and cons of cloud event streaming services on GCP, Azure, and Confluent Cloud. He also compares major stream processing and messaging services: Cloud Pub/Sub vs. Azure vs. Confluent Cloud, and outlines major differences among them.