Red Hat AMQ

Cloud-native messaging on Red Hat OpenShift with Quarkus and AMQ Online

Cloud-native messaging on Red Hat OpenShift with Quarkus and AMQ Online

Quarkus is a Kubernetes-native Java stack tailored for GraalVM and OpenJDK HotSpot, crafted from the best of breed Java libraries and standards, according to the project website. Starting with the 0.17.0 release, Quarkus supports using the Advanced Message Queuing Protocol (AMQP), which is an open standard for passing business messages between applications or organizations.

Red Hat AMQ Online is a Red Hat OpenShift-based mechanism for delivering messaging as a managed service. Previously, we have seen how to use AMQ Online to provision messaging. In this article, we will combine AMQ Online and Quarkus to show how you can create a modern messaging setup on OpenShift using two new technologies from the messaging space.

The guide assumes you have an installation of AMQ Online on OpenShift. Read the installation guide for more information. AMQ Online is based on the EnMasse open source project.

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CDC pipeline with Red Hat AMQ Streams and Red Hat Fuse

CDC pipeline with Red Hat AMQ Streams and Red Hat Fuse

Change Data Capture (CDC) is a pattern that enables database changes to be monitored and propagated to downstream systems. It is an effective way of enabling reliable microservices integration and solving typical challenges, such as gradually extracting microservices from existing monoliths.

With the release of Red Hat AMQ Streams 1.2, Red Hat Integration now includes a developer preview of CDC features based on upstream project Debezium.

This article explains how to make use of Red Hat Integration to create a complete CDC pipeline. The idea is to enable applications to respond almost immediately whenever there is a data change. We capture the changes as they occur using Debezium and stream it using Red Hat AMQ Streams. We then filter and transform the data using Red Hat Fuse and send it to Elasticsearch, where the data can be further analyzed or used by downstream systems.

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Get started with reactive programming with creative Coderland tutorials

Get started with reactive programming with creative Coderland tutorials

The Reactica roller coaster is the latest addition to Coderland, our fictitious amusement park for developers. It illustrates the power of reactive computing, an important architecture for working with groups of microservices that use asynchronous data to work with each other.

In this scenario, we need to build a web app to display the constantly updated wait time for the coaster.

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Developer preview of Debezium Apache Kafka connectors for Change Data Capture (CDC)

Developer preview of Debezium Apache Kafka connectors for Change Data Capture (CDC)

With the release of Red Hat AMQ Streams 1.2, Red Hat Integration now includes a developer preview of Change Data Capture (CDC) capabilities to enable data integration for modern cloud-native microservices-based applications. CDC features are based on the upstream project Debezium and are natively integrated with Apache Kafka and Strimzi to run on top of Red Hat OpenShift Container Platform, the enterprise Kubernetes, as part of the AMQ Streams release.

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Building Apache Kafka Streams applications using Red Hat AMQ Streams: Part 2

Building Apache Kafka Streams applications using Red Hat AMQ Streams: Part 2

The Apache Kafka project includes a Streams Domain-Specific Language (DSL) built on top of the lower-level Stream Processor API. This DSL provides developers with simple abstractions for performing data processing operations. However, how one builds a stream processing pipeline in a containerized environment with Kafka isn’t clear. This second article in a two-part series uses the basics from the previous article to build an example application using Red Hat AMQ Streams.

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Building Apache Kafka Streams applications using Red Hat AMQ Streams: Part 1

Building Apache Kafka Streams applications using Red Hat AMQ Streams: Part 1

The Apache Kafka project includes a Streams Domain-Specific Language (DSL) built on top of the lower-level Stream Processor API. This DSL provides developers with simple abstractions for performing data processing operations. However, how to build a stream processing pipeline in a containerized environment with Kafka isn’t clear. This two-part article series describes the steps required to build your own Apache Kafka Streams application using Red Hat AMQ Streams.

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Accessing Apache Kafka in Strimzi: Part 5 – Ingress

Accessing Apache Kafka in Strimzi: Part 5 – Ingress

In the fifth and final part of this series, we will look at exposing Apache Kafka in Strimzi using Kubernetes Ingress. This article will explain how to use Ingress controllers on Kubernetes, how Ingress compares with Red Hat OpenShift routes, and how it can be used with Strimzi and Kafka. Off-cluster access using Kubernetes Ingress is available only from Strimzi 0.12.0. (Links to previous articles in the series can be found at the end.)

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Accessing Apache Kafka in Strimzi: Part 4 – Load balancers

Accessing Apache Kafka in Strimzi: Part 4 – Load balancers

In this fourth article of our series about accessing Apache Kafka clusters in Strimzi, we will look at exposing Kafka brokers using load balancers. (See links to previous articles at end.) This article will explain how to use load balancers in public cloud environments and how they can be used with Apache Kafka.

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Accessing Apache Kafka in Strimzi: Part 3 – Red Hat OpenShift routes

Accessing Apache Kafka in Strimzi: Part 3 – Red Hat OpenShift routes

In the third part of this article series (see links to previous articles below), we will look at how Strimzi exposes Apache Kafka using Red Hat OpenShift routes. This article will explain how routes work and how they can be used with Apache Kafka. Routes are available only on OpenShift, but if you are a Kubernetes user, don’t be sad; a forthcoming article in this series will discuss using Kubernetes Ingress, which is similar to OpenShift routes.

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Accessing Apache Kafka in Strimzi: Part 2 – Node ports

Accessing Apache Kafka in Strimzi: Part 2 – Node ports

This article series explains how Apache Kafka and its clients work and how Strimzi makes it accessible for clients running outside of Kubernetes. In the first article, we provided an introduction to the topic, and here we will look at exposing an Apache Kafka cluster managed by Strimzi using node ports.

Specifically, in this article, we’ll look at how node ports work and how they can be used with Kafka. We also will cover the different configuration options available to users and the pros and cons of using node ports.

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