When it comes time to choose a distributed messaging system, everyone knows the answer: Apache Kafka. But how about when you’re on the hook to choose a world-class, horizontally scalable stream data processing system? When you need not just publish and subscribe messaging, but also long-term storage, a flexible integration framework, and a means of deploying real-time stream processing applications at scale without having to integrate many different pieces of infrastructure yourself? The answer is still Apache Kafka.
In this talk, Viktor will give a rapid-fire review of the breadth of Kafka as a streaming data platform. You’ll see its internal architecture, including how it partitions messaging workloads in a fault-tolerant way, and how it provides message durability. Viktor will explain Kafka’s approach to pub/sub messaging and how the Confluent .NET client offers a framework for computation over streaming data.