In the 1980s, the spreadsheet was considered such a valuable tool that would make buying a computer just to run it worthwhile. The term coined to describe such an application was a killer app. Is there an equivalent for Red Hat OpenShift? When it comes to tools that boost the value of the platform as a whole, we need to look at Red Hat OpenShift operators . There are generally speaking 2 types of operators. Some install and run an app (e. , a database or a message queue) on an OpenShift cluster for you, then keep managing it by watching resources and acting automatically as needed.
Others teach your cluster entirely new concepts, like GitOps, CI/CD pipelines or certificate management, and expose them declaratively via custom resource definitions (CRDs). We’ve picked 5 of the most important operators that really showcase what Red Hat OpenShift is capable of. Of all modern development and deployment models, GitOps is perhaps the most popular. By solidifying on Git as the single source of truth (for example, based on GitHub and GitLab), GitOps provides a declarative framework ideal for managing containerized workloads for both cloud-native applications and AI.
It inherently provides an audit record of all changes and continuously prevents drift arising from manual changes applied directly to the cluster. With security and reliability remaining top priorities for engineering, GitOps workflows automate both infrastructure maintenance and deployment testing. By integrating Argo Rollouts into those workflows, teams can easily execute progressive deployment strategies, such as canary and blue-green releases, to test and ship updates with zero downtime.
The Red Hat OpenShift GitOps operator is our productized version of the Argo CD upstream project and enables developers and administrators alike to take full advantage of this model, driving streamlined application delivery for dev teams and consistent cluster configuration for platform operations. It’s easy to see logging as a type of plumbing activity. But those logs you’re saving aren’t just a tool for finding out why a pod is crash-looping; they also contain a wealth of information about how your system is performing.