By the end of this course, students will be able to:Understand the core concepts of OpenShift including projects, pods, services, and routes, and how they interact to run applications efficiently in a containerized environment.Configure and manage environment variables, ConfigMaps, and Secrets to customize applications for different deployment scenarios and ensure secure handling of sensitive information.Work with storage in OpenShift, including Persistent Volumes (PVs), Persistent Volume Claims (PVCs), and different storage backends, allowing applications to maintain data persistence across pod restarts.Expose applications securely using Routes, NodePorts, and Load Balancer Services (including MetalLB), ensuring proper external access while following best practices for security and scalability.Differentiate between Stateless and Stateful applications in Kubernetes/OpenShift and deploy them correctly using Deployments and StatefulSets, understanding when and why each type is appropriate.Prepare for the Red Hat OpenShift Administration (EX280) certification exam with hands-on examples, practical exercises, and real-world use cases that build confidence and readiness for professional environments.Gain the ability to troubleshoot common OpenShift issues, optimize application deployments, and implement industry-standard practices for managing workloads in production-grade clusters.By completing this course, learners will acquire a strong foundation to advance in cloud-native technologies, OpenShift administration, and container orchestration careers.
What you'll learn
Understand core concepts of OpenShift
Configure environment variables and manage sensitive information
Work with storage in OpenShift
Expose applications securely
Differentiate between stateless and stateful applications
Prepare for the Red Hat OpenShift Administration certification exam
Troubleshoot OpenShift issues
Course objectives
Provide hands-on examples and practical exercises
Ensure readiness for professional environments
Teach best practices for managing workloads in production-grade clusters