Apache Kafka® on Kubernetes - A match made in heaven!
2/8/19, 1:00 PM - 2/8/19, 1:45 PM
- Cloud-Native Kubernetes Microservice Apache Kafka Kafka Streaming
Workshop Room 020
Anatoly Zelenin, Arash Kaffamanesh, Kubernauts Community
Abstract:
Apache Kafka® is used for building real-time data pipelines and streaming apps. It is horizontally scalable, fault-tolerant, and can be used for Stream Processing, as a Storage or Messaging System and more.
Running and operating Stateful apps on Kubernetes is not easy, at least if you’re going to deal with replication and have to take care of syncing and re-balancing your streaming data on different nodes and / or different clusters in different regions.
Kubernetes is about Resiliency and Scale, Kafka too! Kafka is Stateful, Kubernetes' support for Statefulsets has reached a mature state! There are many reasons why one should run Kafka on K8s. But that's not easy!
In this talk we provide a short introduction to Apache Kafka and walk you through the steps to deploy Apache Kafka with Kafka Confluent Platform Helm Charts and Strimzi Kafka Operator on Rancher Kubernetes Engine, Microsoft's AKS and OpenShift and let you decide which option is the right choice for your use case and budget!
Apache Kafka on Kubernetes, a match made in heaven, but it's not easy!
Anatoly's Bio:
As a Computer Scientist, Anatolys goal is to bring theoretical computer science and practical software engineering together. Standing on the shoulders on giants he combines novel technology with rock-solid and battle-tested approaches to create new and powerful systems. Given his broad Computer Science and development background, Anatoly consults large enterprises and middle-sized businesses how to architecture applications in an always-changing environment.
Arash’s Bio:
Arash works on different Kubernetes Upstream, OpenShift and Rancher projects on AWS, Azure and OpenStack in parallel. He is the founder of Clouds Sky and Kubernauts, one of the organizers of KubeCologne Conference. He loves to learn in communities and share the knowledge learned.