Quarkus

Cloud-native modernization or death? A false dichotomy

Cloud-native modernization or death? A false dichotomy

DevNation Tech Talks are hosted by the Red Hat technologists who create our products. These sessions include real solutions plus code and sample projects to help you get started. In this talk, you’ll learn about cloud-native modernization from Daniel Oh and Burr Sutter.

Are you familiar with the tight coupling of applications with their underlying platform that makes change hard? Or, coupling that creates a lack of scalability, performance, and flexibility for existing applications built with legacy technology? How about the fact that re-architecting applications cannot be done overnight?

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How I built a serverless blog search with Java, Quarkus, and AWS Lambda

How I built a serverless blog search with Java, Quarkus, and AWS Lambda

DevNation Tech Talks are hosted by the Red Hat technologists who create our products. These sessions include real solutions plus code and sample projects to help you get started. In this talk, you’ll learn about serverless blog search with Java, Quarkus, and AWS Lambda from Gunnar Morling and Burr Sutter.

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New features in Red Hat CodeReady Studio 12.17 GA and JBoss Tools 4.17.0 Final for Eclipse 2020-09

New features in Red Hat CodeReady Studio 12.17 GA and JBoss Tools 4.17.0 Final for Eclipse 2020-09

JBoss Tools 4.17.0 and Red Hat CodeReady Studio 12.17 for Eclipse 4.17 (2020-09) are now available. For this release, we focused on improving Quarkus and container-based development and fixing bugs. We also updated the Hibernate Tools runtime provider and Java Developer Tools (JDT) extensions, which are now compatible with Java 15. Additionally, we made many changes to platform views, dialogs, and toolbars in the user interface (UI).

Keep reading for an overview of what’s new in JBoss Tools 4.17.0 and CodeReady Studio 12.17 for Eclipse 4.17 (2020-09).

Installation

First, let’s look at how to install these updates. CodeReady Studio (previously Red Hat Developer Studio) comes with everything pre-bundled in its installer. Download the installer from the Red Hat CodeReady Studio product page and run it as follows:

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Securely connect Quarkus and Red Hat Data Grid on Red Hat OpenShift

Securely connect Quarkus and Red Hat Data Grid on Red Hat OpenShift

The release of Red Hat Data Grid 8.1 offers new features for securing applications deployed on Red Hat OpenShift. Naturally, I wanted to check them out for Quarkus. Using the Quarkus Data Grid extension made that easy to do.

Data Grid is an in-memory, distributed, NoSQL datastore solution based on Infinispan. Since it manages your data, Data Grid should be as secure as possible. For this reason, it uses a default property realm that requires HTTPS and automatically enforces user authentication on remote endpoints. As an additional layer of security on OpenShift, Data Grid presents certificates signed by the OpenShift Service Signer. In practice, this means that Data Grid is as secure as possible out of the box, requiring encrypted connections and authentication from the first request. Data Grid generates a default set of credentials (which, of course, you can override), but unauthenticated access is denied.

In this article, I show you how to configure a Quarkus application with Data Grid and deploy it on OpenShift.

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Building modern CI/CD workflows for serverless applications with Red Hat OpenShift Pipelines and Argo CD, Part 2

Building modern CI/CD workflows for serverless applications with Red Hat OpenShift Pipelines and Argo CD, Part 2

In the first half of this article, I introduced Tekton as a framework for cloud-native CI/CD pipelines, and Argo CD as its perfect partner for GitOps on Red Hat OpenShift. Our example for the demonstration is a Knative service that deploys and serves a Quarkus application. Our goal is to develop a complete continuous integration and delivery process, which begins when a commit is initiated in the application’s GitHub repository and ends with the new application version deployed in the development, staging, and production environments.

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Build a data streaming pipeline using Kafka Streams and Quarkus

Build a data streaming pipeline using Kafka Streams and Quarkus

In typical data warehousing systems, data is first accumulated and then processed. But with the advent of new technologies, it is now possible to process data as and when it arrives. We call this real-time data processing. In real-time processing, data streams through pipelines; i.e., moving from one system to another. Data gets generated from static sources (like databases) or real-time systems (like transactional applications), and then gets filtered, transformed, and finally stored in a database or pushed to several other systems for further processing. The other systems can then follow the same cycle—i.e., filter, transform, store, or push to other systems.

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Quarkus and Jakarta EE: Together, or not?

Quarkus and Jakarta EE: Together, or not?

In this article, I answer a question that I have seen asked on various forums: Will Quarkus be compatible with Jakarta EE? To understand our answer to that question, it is helpful to know the history of Quarkus and what we’re trying to achieve with it. So, please indulge me while I lay that groundwork.

A short history of Quarkus and Java EE

When Emmanuel Bernard, Jason Greene, Bob McWhirter, and I first discussed kicking off the ThornFly.x proof of concept, which would later become Quarkus, we had conversations about where Java EE (now Jakarta EE) would eventually fit. I think we all agreed that we already had the best open source implementation of Java EE in the form of WildFly and Red Hat JBoss Enterprise Application Platform (JBoss EAP). Creating yet another addition to this space seemed confusing at best. At worst, we feared that it would split our engineering and open source community efforts.

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