Kubernetes vs. OpenShift: Choosing DevOps and CI/CD Workflows

· Source: IBM Technology · Field: Technology & Digital — Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Intermediate, quick

Summary

Kubernetes serves as a foundational container orchestration system, handling scheduling, scaling, and connectivity across clusters. OpenShift, however, builds upon Kubernetes by integrating a full platform that includes building tools, automated workflows, operators, and ready-to-use configurations, thereby simplifying the entire DevOps and CI/CD process. While running Kubernetes on its own requires teams to construct the entire platform, managing pipelines, security, and admission tools, OpenShift offers a "one simple flow." This platform facilitates a streamlined development workflow where code pushes trigger pipelines, container images are built and sent to a registry, and then deployed via image streams. For operations teams, OpenShift provides a web console for cluster management, resource monitoring, and scaling applications by adding machines or pods, supporting deployment across public cloud, private cloud, VMware, and bare metal environments.

Key takeaway

For DevOps Engineers evaluating container orchestration platforms, OpenShift offers a more integrated solution than bare Kubernetes. If your goal is to accelerate development and operations, consider its benefits. It simplifies CI/CD workflows, automates image management, and provides a unified console for cluster monitoring and scaling. This integration reduces the overhead of building and maintaining a custom platform. It allows your team to focus on application delivery rather than infrastructure management.

Key insights

OpenShift extends Kubernetes into a comprehensive, integrated platform for streamlined DevOps and CI/CD workflows.

Principles

Method

OpenShift's workflow involves code push triggering a pipeline, building a container image, sending it to an image registry, and deploying it via an image stream.

In practice

Topics

Best for: CTO, VP of Engineering/Data, Director of AI/ML, DevOps Engineer, MLOps Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by IBM Technology.