Matthias Sax

Apache Kafka’s scalability and resilience depend not just on its brokers, but crucially on its client ecosystem—most notably, Kafka Streams. Maintaining stability forces Streams applications to redistribute partitioned workloads during infrastructure shifts. These rebalances are notorious for causing operational friction, performance degradation, and unexpected downtime. KIP-1071 (Streams groups) introduces a major evolutionary step towards resolving this friction. Built on the foundation of next-generation consumer groups (KIP-848), Streams groups move task assignment to the broker side and enable fully incremental rebalancing. The result is faster, more stable, and predictable coordination that fundamentally changes how Kafka Streams handles scaling in, scaling out, and failing over. In this talk, we will dive into the core assignment logic, specifically focusing on the performance of the new protocol, its sticky task assignor, and a direct comparison to classic rebalancing. We will also explore a completely new incarnation of the high-availability assignor, which is designed explicitly to allow smooth scaling out for state-heavy applications. You will see how this updated…

Explore the past achievements, present realities, and future possibilities of Kafka/Kafka Streams with a mix of community leaders, contributors, and Apache Kafka committers. This is a panel so audience interaction is central and encouraged! Bring us your questions, challenge assumptions, and help shape the future of Kafka/Kafka Streams with this session

There is basically no stream processing without windowing, and Kafka Streams provides a rich set of built-in windows for aggregations and joins. However, it is often unclear to developers how different window types works, and even more important for what use-case a specific window is a good fit. In particular, sliding windows are often a mystery, and are easily confused with hopping windows. In this talk, we will explain the different window types of Kafka Streams, give guidance when to use what window, and unriddle the curious case of the sliding window. Furthermore, we give a sneak preview into the new "BatchWindow" type, that was proposed via KIP-1127 recently, which unlocks new use-cases that where hard to cover in the past. -- Join this session to become a windowing expert and set yourself up for success with Kafka Streams.

Let’s be honest: who wants to have more than one client to connect to a data system? Now consider Apache Kafka. It ships with four different Java clients: producer, consumer, admin, and streams. Want to create a topic in a producer application? Use the admin client and the producer client. Want to produce and consume? Either use the producer and the consumer, or use Kafka Streams. So how did we get here? And more importantly: how can we simplify it? Are incremental improvements enough? In this talk, we’ll propose a radical approach: a single unified Java client built from scratch for producing, consuming, processing, and administration tasks. We take you on a brainstorming session about what we can and cannot do, and what we want to achieve. How can we make simple things easy and difficult things possible? What does a modern Java API look like, using the standard library, a reasonable threading model, lambdas, and futures for async calls? We think it's high time that we take another look at the Java clients and build a client ready for the next decade. Come and join the conversation about the future of Kafka clients.

Let’s be honest: who wants to have more than one client to connect to a data system? Now consider Apache Kafka. It ships with four different Java clients: producer, consumer, admin, and streams. Want to create a topic in a producer application? Use the admin client and the producer client. Want to produce and consume? Either use the producer and the consumer, or use Kafka Streams. So how did we get here? And more importantly: how can we simplify it? Are incremental improvements enough? In this talk, we’ll propose a radical approach: a single unified Java client built from scratch for producing, consuming, processing, and administration tasks. We take you on a brainstorming session about what we can and cannot do, and what we want to achieve. How can we make simple things easy and difficult things possible? What does a modern Java API look like, using the standard library, a reasonable threading model, lambdas, and futures for async calls? We think it's high time that we take another look at the Java clients and build a client ready for the next decade. Come and join the conversation about the future of Kafka clients.