Since the first Red Hat OpenShift release in 2015, Red Hat has put out numerous releases based on Kubernetes. Five years later, Kubernetes is celebrating its sixth birthday, and last month, we announced the general availability of Red Hat OpenShift Container Platform 4.5. In this article, I offer a high-level view of the latest OpenShift release and its technology and feature updates based on Kubernetes 1.18.
Continue reading OpenShift 4.5: Bringing developers joy with Kubernetes 1.18 and so much more
Apache Kafka has emerged as the leading platform for building real-time data pipelines. Born as a messaging system, mainly for the publish/subscribe pattern, Kafka has established itself as a data-streaming platform for processing data in real-time. Today, Kafka is also heavily used for developing event-driven applications, enabling the services in your infrastructure to communicate with each other through events using Apache Kafka as the backbone. Meanwhile, cloud-native application development is gathering more traction thanks to Kubernetes.
Thanks to the abstraction layer provided by this platform, it’s easy to move your applications from running on bare metal to any cloud provider (AWS, Azure, GCP, IBM, and so on) enabling hybrid-cloud scenarios as well. But how do you move your Apache Kafka workloads to the cloud? It’s possible, but it’s not simple. You could learn all of the Apache Kafka tools for handling a cluster well enough to move your Kafka workloads to Kubernetes, or you could leverage the Kubernetes knowledge you already have using Strimzi.
Note: Strimzi will be represented at the virtual KubeCon Europe 2020 conference from 17-20 August 2020. See the end of the article for details.
Continue reading “Introduction to Strimzi: Apache Kafka on Kubernetes (KubeCon Europe 2020)”
Apache Kafka is a rock-solid, super-fast, event streaming backbone that is not only for microservices. It’s an enabler for many use cases, including activity tracking, log aggregation, stream processing, change-data capture, Internet of Things (IoT) telemetry, and more.
Red Hat AMQ Streams makes it easy to run and manage Kafka natively on Red Hat OpenShift. AMQ Streams’ upstream project, Strimzi, does the same thing for Kubernetes.
Setting up a Kafka cluster on a developer’s laptop is fast and easy, but in some environments, the client setup is harder. Kafka uses a TCP/IP-based proprietary protocol and has clients available for many different programming languages. Only the JVM client is on Kafka’s main codebase, however.
Continue reading “HTTP-based Kafka messaging with Red Hat AMQ Streams”
In just a matter of weeks, the world that we knew changed forever. The COVID-19 pandemic came swiftly and caused massive disruption to our healthcare systems and local businesses, throwing the world’s economies into chaos. The coronavirus quickly became a crisis that affected everyone. As researchers and scientists rushed to make sense of it, and find ways to eliminate or slow the rate of infection, countries started gathering statistics such as the number of confirmed cases, reported deaths, and so on. Johns Hopkins University researchers have since aggregated the statistics from many countries and made them available.
In this article, we demonstrate how to build a website that shows a series of COVID-19 graphs. These graphs reflect the accumulated number of cases and deaths over a given time period for each country. We use the Red Hat build of Quarkus, Apache Camel K, and Red Hat AMQ Streams to get the Johns Hopkins University data and populate a MongoDB database with it. The deployment is built on the Red Hat OpenShift Container Platform (OCP).
Continue reading “Tracking COVID-19 using Quarkus, AMQ Streams, and Camel K on OpenShift”
Apache Kafka is one of the most used pieces of software in modern application development because of its distributed nature, high throughput, and horizontal scalability. Every day more and more organizations are adopting Kafka as the central event bus for their event-driven architecture. As a result, more and more data flows through the cluster, making the connectivity requirements rise in priority for any backlog. For this reason, the Apache Camel community released the first iteration of Kafka Connect connectors for the purpose of easing the burden on development teams.
Continue reading “Extending Kafka connectivity with Apache Camel Kafka connectors”
Want to smoothly modernize your legacy and monolithic applications to microservices or cloud-native without writing any code? Through this demonstration, we show you how to achieve the following change data capture scenario between two microservices on Red Hat OpenShift using the combination of Syndesis, Strimzi, and Debezium.
Continue reading “Change data capture for microservices without writing any code”
One question always comes up as organizations moving towards being cloud-native, twelve-factor, and stateless: How do you get an organization’s data to these new applications? There are many different patterns out there, but one pattern we will look at today is change data capture. This post is a simple how-to on how to build out a change data capture solution using Debezium within an OpenShift environment. Future posts will also add to this and add additional capabilities.
Continue reading Change data capture with Debezium: A simple how-to, Part 1
Thanks to changes in Apache Kafka 2.4.0, consumers are no longer required to connect to a leader replica to consume messages. In this article, I introduce you to Apache Kafka’s new
ReplicaSelector interface and its customizable
RackAwareReplicaSelector. I’ll briefly explain the benefits of the new rack-aware selector, then show you how to use it to more efficiently balance load across Amazon Web Services (AWS) availability zones.
For this example, we’ll use Red Hat AMQ Streams with Red Hat OpenShift Container Platform 4.3, running on Amazon AWS.
Continue reading “Consuming messages from closest replicas in Apache Kafka 2.4.0 and AMQ Streams”
Change data capture, or CDC, is a well-established software design pattern for a system that monitors and captures the changes in data so that other software can respond to those changes. CDC captures row-level changes to database tables and passes corresponding change events to a data streaming bus. Applications can read these change event streams and access these change events in the order in which they occurred.
Thus, change data capture helps to bridge traditional data stores and new cloud-native event-driven architectures. Meanwhile, Debezium is a set of distributed services that captures row-level changes in databases so that applications can see and respond to those changes. This general availability (GA) release from Red Hat Integration includes the following Debezium connectors for Apache Kafka: MySQL Connector, PostgreSQL Connector, MongoDB Connector, and SQL Server Connector.
Continue reading “Capture database changes with Debezium Apache Kafka connectors”
If you read the first article in this series, then you already set up the example application you’ll need for this article. If you have not set up the population health management application, you should do that before continuing. In this article, we’ll run a few business processes through our event- and business-process-driven application to test it out.
Continue reading Running an event-driven health management business process through end user scenarios: Part 2