Stream processing: Continuous data management in real time
Stream processing lets developers view, analyze, and combine data from a wide variety of sources.
Stream processing lets developers view, analyze, and combine data from a wide variety of sources.
This article explores use cases for the dynamic quorum configuration in Kafka...
Deploy a fully functional streams for Apache Kafka cluster in an automated...
This solution demonstrates an implementation to build a platform that...
Open source is at the heart of everything Red Hat works on. Learn how Red Hat...
This article explores use cases for the dynamic quorum configuration in Kafka that allows KRaft clusters to scale controller nodes without downtime.
Deploy a fully functional streams for Apache Kafka cluster in an automated fashion using Ansible.
Ansible Collection for Red Hat Runtimes products comes in two different flavors: one upstream, one downstream. This article explores what this means exactly.
This solution demonstrates an implementation to build a platform that synthesizes conversations across multiple different communication channels and services.
Open source is at the heart of everything Red Hat works on. Learn how Red Hat engineers contribute to data streaming projects such as Apache Kafka and Strimzi.
Learn how to build an end-to-end reactive stream processing application using Apache Kafka as an event streaming platform, Quarkus for your backend, and a frontend written in Angular.