Join Kris Jenkins and guests from the community as they discuss the latest Apache Kafka® news, use cases, and trends spanning the topics of data streaming, microservices, modern IT architectures, and the cloud.
What are some recommendations to consider when running Apache Kafka in production? Jun Rao, one of the original Kafka creators, as well as an ongoing committer and PMC member, shares the essential wisdom he's gained from developing Kafka and dealing with a large number of Kafka use cases.
Is it possible to build a real-time data platform without using stateful stream processing? Forecasty.ai is an artificial intelligence platform for forecasting commodity prices, imparting insights into the future valuations of raw materials for users. Nearly all AI models are batch-trained once, but precious commodities are linked to ever-fluctuating global financial markets, which require real-time insights. In this episode, Ralph Debusmann (CTO, Forecasty.ai) shares their journey of migrating from a batch machine learning platform to a real-time event streaming system with Apache Kafka and delves into their approach to making the transition frictionless.
Java Virtual Machines (JVMs) impact Apache Kafka performance in production. How can you optimize your event-streaming architectures so they process more Kafka messages using the same number of JVMs? Gil Tene (CTO and Co-Founder, Azul) delves into JVM internals and how developers and architects can use Java and optimized JVMs to make real-time data pipelines more performant and more cost effective, with use cases.
Apache Kafka 3.3 is released! With over two years of development, KIP-833 marks KRaft as production ready for new AK 3.3 clusters only. On behalf of the Kafka community, Danica Fine (Senior Developer Advocate, Confluent) shares highlights of this release, with KIPs from Kafka Core, Kafka Streams, and Kafka Connect.
How do you set data applications in motion by running stateful business logic on streaming data? Capturing key stream processing events and cumulative statistics that necessitate real-time data assessment, migration, and visualization remains as a gap—for event-driven systems and stream processing frameworks according to Fred Patton (Developer Evangelist, Swim Inc.) In this episode, Fred explains streaming applications and how it contrasts with stream processing applications. Fred and Kris also discuss how you can use Apache Kafka and Swim for a real-time UI for streaming data.
Kris Jenkins is a senior developer advocate for Confluent, a veteran contractor, and former CTO and co-founder of a gold-trading business. He's especially interested in software design, functional programming, real-time systems, and electronic music.
If there's something you want to know about Apache Kafka, Confluent or event streaming, please send us an email with your question and we'll hope to answer it on the next episode of Ask Confluent.Email Us