Integration

Explore this year's most popular articles on Kafka data storage and migration, API management, software security, identity and access management, and more.

Red Hat build of Apache Camel 4.8 brings enhancements in contract-first API development, optimized OpenShift-based workflows, and improved developer tooling.

Explore the possibilities of upgrading the Red Hat build of Apache Camel on

Learn how a developer can work with RAG and LLM leveraging their own data chat for queries.

This article will guide you through the process of rapid prototyping using Apache Camel, from the initial concept to the final deployment on OpenShift, all within a fully web-based, low-code interface.

Connect, secure, and protect your distributed Kubernetes services with

Discover 5 potential use cases for Event-Driven Ansible, from network management and latency reduction to running health checks on your infrastructure.

In this session, we will introduce the idea of a Virtual Application Network and an implementation of this idea in the Skupper project.

This is a really quick demo to show you how load balancing across different Red Hat OpenShift clusters can work really easily with Skupper.

In this session, we are going to conduct a live demonstration of hybrid cloud-native Java microservices, distributed across Amazon Web Services, Google Cloud Platform, and Microsoft Azure, with real-time load-balancing and fail-over.

Ask an OpenShift Admin about Red Hat Service Interconnect.

Nowadays, everyone wants distributed applications to run over many clouds while acting as though everything is running locally. To deploy your Kubernetes application across different clusters, you must either expose your services to the public internet or configure a VPN.
Red Hat Service Interconnect provides a third option. It links clusters to safe layer 7 networks. It sends local service traffic to remote clusters through the network. For example, you can host your database services on a private cluster while maintaining full connectivity with public cloud services. Application Interconnect provides highly accessible connectivity at scale by utilizing redundant network channels and clever routing.
In this session, we will demonstrate the fundamental notions that enable Service Interconnect to process hybrid cloud calls across services and will begin with a simple demonstration.

Red Hat Service Interconnect enables application and service connectivity across

Welcome to the Q3 edition of Red Hat’s quarterly newsletter all about Apache

Enables cryptographic signing, verification of software, and provenance metadata

This tutorial gives you a unique chance to learn, hands-on, some of the basics of large language models (LLMs) in the Developer Sandbox for Red Hat OpenShift.

In today's fast-paced IT landscape, the need for efficient and effective

This blog post summarizes an experiment to extract structured data from

Learn how to deploy and use the Multi-Cloud Object Gateway (MCG) from Red Hat OpenShift Data Foundation to support development and testing of applications and Artificial Intelligence (AI) models which require S3 object storage.

This article explains how to use Red Hat OpenShift AI in the Developer Sandbox for Red Hat OpenShift to create and deploy models.

Dive into the second edition of Camel integration quarterly digest, covering the

While the VMDK format is open source, they are traditionally used within the VMware family of solutions, such as VMware Workstation and VMware vSphere. In this post, we will describe how the previously created VMDK disk can be used as the basis for a virtual machine hosted in a VMware vSphere environment.

Learn how to use image mode for Red Hat Enterprise Linux to create a Virtual Machine Disk (VMDK) file for use within VMware vSphere environments.

This example demonstrates how to use the OpenShift Cluster Observability Operator, which provides a full monitoring stack you can configure to meet your needs.

Explore how to use OpenVINO Model Server (OVMS) built on Intel's OpenVINO toolkit to streamline the deployment and management of deep learning models.