In this article, I share several new language support features in the recently released Language Support for Apache Camel VS Code extension 0.0.27. Before I discuss these improvements, please note that updates to the VS Code extension are available in other IDEs that support the Camel Language Server, including Eclipse IDE, Eclipse Che, and more. It is simply easier to focus on one IDE for my demonstrations, so I’ve chosen VS Code.
Note: Apache Camel is a versatile open source integration framework based on known enterprise integration patterns.
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Apache Camel K should be as lightweight as possible. Therefore, the Camel K project provides standalone Java files that describe a Camel integration. The downside to this practice is that existing IDEs cannot provide complete support out of the box. A few months ago, I mentioned the Java language support for Apache Camel K that was discussed in Red Hat Visual Studio Code (VS Code) extension, and how it provides Java language support for Apache Camel K. In this article and demo, I show you how to do the same with Eclipse Che and che.openshift.io.
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Change data capture (CDC) is a well-established software design pattern for a system that monitors and captures data changes so that other software can respond to those events. Using KafkaConnect, along with Debezium Connectors and the Apache Camel Kafka Connector, we can build a configuration-driven data pipeline to bridge traditional data stores and new event-driven architectures.
This article walks through a simple example.
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You might not need Secure Socket Layer (SSL)-based communication between microservices in the same cluster, but it’s often a requirement if you want to connect to a remote web service or message broker. In cases where you will expose a web service or other endpoints, you might also have to use a custom keystore in a microservice deployed on Red Hat OpenShift, so that external clients only connect with a specific truststore.
In this article, I show you how to configure a keystore and a truststore for a Java-based microservice built with Spring Boot. I used the Apache Camel and CXF libraries from Red Hat Fuse to develop the microservice. I used a source-to-image (S2I) deployment and tested the examples in Red Hat OpenShift 4.3.
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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.
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Apache Camel K is a lightweight cloud-integration platform that runs natively on Kubernetes and, in particular, lets you automate your cloud configurations. Based on the famous Apache Camel, Camel K is designed and optimized for serverless and microservices architectures. In this article, I discuss six ways that Camel K transforms how developers work with Kubernetes, Red Hat OpenShift, and Knative on cloud platforms.
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With the rise of social networks and people having more free time due to isolation, it has become popular to see lots of maps and graphs. These are made using big spatial data to explain how COVID-19 is expanding, why it is faster in some countries, and how we can stop it.
Continue reading Working with big spatial data workflows (or, what would John Snow do?)
The Operator SDK makes it simple to build Kubernetes-native applications, providing the tools to build, test, and package Operators. The SDK also helps the developer to build Operators without requiring knowledge of Kubernetes API complexities.
In this article, we will create a sample Operator for deploying a sample application based on Spring Boot and Camel. This application is a simple Camel route that uses the undertow component. After building the Operator, we will deploy it on an OpenShift cluster.
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The rise of microservices-oriented architecture brought us new development paradigms and mantras about independent development and decoupling. In such a scenario, we have to deal with a situation where we aim for independence, but we still need to react to state changes in different enterprise domains.
I’ll use a simple and typical example in order to show what we’re talking about. Imagine the development of two independent microservices:
User. We designed them to expose a REST interface and to each use a separate database, as shown in Figure 1:
Figure 1: Order and User microservices.
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