Kubernetes
The standard platform for orchestrating containerized applications at scale — managing deployment, scaling, and self-healing across clusters.
Kubernetes (K8s) has become the standard for running containerized workloads at scale, handling automatic deployment, scaling, and recovery of containerized applications across clusters of machines. It's required for DevOps and platform engineering roles and increasingly expected of senior backend engineers at companies running microservices architectures.
Typical time to job-readiness: ~3 months.
Learning Kubernetes
Beginner
Understand pods, deployments, services, and namespaces. Use kubectl to deploy an app to a local cluster (Minikube or kind) and expose it via a service.
Intermediate
Configure resource limits, liveness/readiness probes, ConfigMaps, Secrets, and horizontal pod autoscaling. Understand Ingress and the different service types.
Advanced
Network policies, RBAC, Helm chart authoring, cluster upgrades, and multi-cluster architecture. Kubernetes is a common deep-dive topic in SRE and platform engineering interviews.
Key concepts
- Pod — smallest deployable unit; one or more containers sharing a network namespace
- Deployment — manages replica sets, rolling updates, and rollback for stateless apps
- Service — stable endpoint (IP/DNS) that load balances traffic to matching pods
- ConfigMap and Secret — inject config and sensitive values without baking them into images
- Liveness vs readiness probes — liveness restarts unhealthy pods, readiness removes them from load balancing
- kubectl — CLI for interacting with clusters; essential for debugging
Common interview topics
- Explain the difference between a Pod, Deployment, and ReplicaSet
- What is the difference between liveness and readiness probes
- How does Kubernetes handle rolling deployments
- What is a Service and what are the differences between ClusterIP, NodePort, and LoadBalancer
- How would you debug a pod that is stuck in CrashLoopBackOff