Red Hat AMQ Streams

Intro to Apache Kafka and Kafka Streams for Event-Driven Microservices on DevNation Live

Intro to Apache Kafka and Kafka Streams for Event-Driven Microservices on DevNation Live

Scalability is often a key issue for many growing organizations. That’s why many organizations use Apache Kafka, a popular messaging and streaming platform. It is horizontally scalable, cloud-native, and versatile. It can serve as a traditional publish-and-subscribe messaging system, as a streaming platform, or as a distributed state store. Companies around the world use Apache Kafka to build real-time streaming applications, streaming data pipelines, and event-driven architectures.

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How to run Kafka on Openshift, the enterprise Kubernetes, with AMQ Streams

How to run Kafka on Openshift, the enterprise Kubernetes, with AMQ Streams

On October 25th Red Hat announced the general availability of their AMQ Streams Kubernetes Operator for Apache Kafka. Red Hat AMQ Streams focuses on running Apache Kafka on Openshift providing a massively-scalable, distributed, and high performance data streaming platform. AMQ Streams, based on the Apache Kafka and Strimzi projects, offers a distributed backbone that allows microservices and other applications to share data with extremely high throughput. This backbone enables:

  • Publish and subscribe: Many to many dissemination in a fault tolerant, durable manner.
  • Replayable events: Serves as a repository for microservices to build in-memory copies of source data, up to any point in time.
  • Long-term data retention: Efficiently stores data for immediate access in a manner limited only by disk space.
  • Partition messages for more horizontal scalability: Allows for organizing messages to maximum concurrent access.

One of the most requested items from developers and architects is how to get started with a simple deployment option for testing purposes. In this guide we will use Red Hat Container Development Kit, based on minishift, to start an Apache Kafka cluster on Kubernetes.

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Welcome Apache Kafka to the Kubernetes Era!

Welcome Apache Kafka to the Kubernetes Era!

We have pretty exciting news this week as Red Hat is announcing the General Availability of their Apache Kafka Kubernetes operator. Red Hat AMQ Streams delivers the mechanisms for managing Apache Kafka on top of OpenShift, our enterprise distribution for Kubernetes.

Everything started last May 2018 when David Ingham (@dingha) unveiled the Developer Preview as new addition to the Red Hat AMQ offering. Red Hat AMQ Streams focuses on running Apache Kafka on OpenShift. In the microservices world, where several components need to rely on a high throughput communication mechanism, Apache Kafka has made a name for itself for being a leading real-time, distributed messaging platform for building data pipelines and streaming applications.

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Smart-Meter Data Processing Using Apache Kafka on OpenShift

Smart-Meter Data Processing Using Apache Kafka on OpenShift

There is a major push in the United Kingdom to replace aging mechanical electricity meters with connected smart meters. New meters allow consumers to more closely monitor their energy usage and associated cost, and they enable the suppliers to automate the billing process because the meters automatically report fine-grained energy use.

This post describes an architecture for processing a stream of meter readings using Strimzi, which offers support for running Apache Kafka in a container environment (Red Hat OpenShift). The data has been made available through a UK research project that collected data from energy producers, distributors, and consumers from 2011 to 2014. The TC1a dataset used here contains data from 8,000 domestic customers on half-hour intervals in the following form:

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Announcing AMQ Streams: Apache Kafka on OpenShift

Announcing AMQ Streams: Apache Kafka on OpenShift

Hi all,

We are excited to announce a Developer Preview of Red Hat AMQ Streams, a new addition to Red Hat AMQ, focused on running Apache Kafka on OpenShift.

Apache Kafka is a leading real-time, distributed messaging platform for building data pipelines and streaming applications.

Using Kafka, applications can:

  • Publish and subscribe to streams of records.
  • Store streams of records.
  • Process records as they occur.

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