Kubernetes Home

Introduction

Welcome to the Kubernetes Course. Kubernetes is the platform teams use to run containers reliably across fleets of machines - from small startups on a managed cluster to global banks running multi-region platforms. This course takes you from first pod to production-grade platform thinking: workloads, networking, storage, security, observability, GitOps, troubleshooting, cloud Kubernetes, and senior interview preparation.

Purpose of this lesson

This course is designed to help learners move beyond copy-paste YAML. You will learn how Kubernetes objects behave, why teams choose them, when they are the wrong tool, and how experienced engineers diagnose real production failures.

Understanding the topic

What you will learn:

• Run containerized applications with Pods, Deployments, Services, Ingress, and autoscaling.
• Operate production clusters with RBAC, probes, resource limits, monitoring, logging, and alerts.
• Debug real incidents: CrashLoopBackOff, OOMKilled, Pending pods, DNS failures, ImagePullBackOff, and cert problems.
• Use platform tools such as Helm, Kustomize, Operators, CRDs, ArgoCD, FluxCD, Prometheus, Grafana, and Istio.

Visual explanation

The course follows the same flow practitioners use when designing and operating a Kubernetes platform:

Container image
  -> workload controller
  -> Service and ingress path
  -> config, secret, storage, and identity
  -> scaling, policy, and observability
  -> GitOps promotion and incident response

Step-by-step explanation

1. Start with the control-plane model: desired state, controllers, scheduler, kubelet, and status.
2. Deploy stateless applications, then layer in Services, Ingress, configuration, storage, and security.
3. Add production operating concerns: probes, resources, autoscaling, monitoring, logging, alerts, and rollout strategies.
4. Practice broken-cluster scenarios until events, logs, metrics, endpoints, and controller status feel natural.
5. Finish with cloud trade-offs, GitOps, platform engineering, capstone projects, and interview-level system design.

Informative example

A minimal Deployment and Service:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: hello-kubernetes
spec:
  replicas: 2
  selector:
    matchLabels:
      app: hello
  template:
    metadata:
      labels:
        app: hello
    spec:
      containers:
        - name: app
          image: nginxdemos/hello:plain-text
---
apiVersion: v1
kind: Service
metadata:
  name: hello
spec:
  selector:
    app: hello
  ports:
    - port: 80
      targetPort: 80

Real-world use

Netflix, Shopify, Spotify, banks, airlines, and SaaS platforms use Kubernetes to standardize deployment, scaling, recovery, and platform operations. The winning teams do not treat Kubernetes as magic; they treat it as a set of APIs and controllers with clear failure modes.

Best practices

• Learn kubectl first, then GitOps and Helm.
• Practice troubleshooting with broken manifests, not only happy-path tutorials.
• Prefer managed Kubernetes for most companies unless cluster operations are your product.

Common mistakes

• Learning only YAML without understanding scheduling, networking, and controllers.
• Skipping resource limits and probes until production.
• Using Kubernetes for a tiny app when a simpler platform would do.

Debugging tips

• When a lesson includes a manifest, always ask: which controller owns this object, what status should change, and which event would prove a failure?
• Build the habit of checking kubectl describe, sorted events, logs, EndpointSlices, rollout status, and resource usage in that order.
• Use a local disposable cluster for destructive practice so production troubleshooting is based on muscle memory, not guesses.

Optimization strategies

• Optimize for reliability before cleverness: correct probes, realistic requests, safe rollouts, and useful alerts beat advanced add-ons.
• Keep platform choices boring until a real requirement appears. Helm, mesh, multi-cluster, and Operators add value only when the team can operate them.
• Treat cost as an engineering signal: right-size requests, use autoscaling carefully, and label workloads for allocation.

Advanced interview questions

Hands-on exercise

Set up a local kind or minikube cluster, deploy the sample app, scale it, break the Service selector, fix it, and write down the exact commands that revealed the problem. This creates the troubleshooting loop used throughout the course.

kind create cluster --name kube-course
kubectl create namespace course-lab
kubectl -n course-lab apply -f hello.yaml
kubectl -n course-lab get deploy,rs,pods,svc,endpointslices
kubectl -n course-lab describe svc hello

Summary

Treat Kubernetes as an operating model for distributed systems. The goal is not to know every field; it is to deploy safely, reason clearly, debug quickly, and design platforms that teams can use without inheriting unnecessary risk.