Cloud Engineering · Kubernetes
Why Every Cloud Engineer Should Learn Kubernetes in 2026
Quick summary — why cloud engineers should learn Kubernetes
Cloud engineers should learn Kubernetes in 2026 because many teams run workloads on managed clusters, and those clusters depend on cloud building blocks: load balancers and ingress for traffic, cloud disks for persistent storage, node autoscaling for capacity and cloud identity for pod permissions. Understanding both sides makes you far better at building and troubleshooting.
In this guide you will learn:
- Managed Kubernetes — and what the cloud handles.
- How services and storage — map to cloud resources.
- Autoscaling and identity — for workloads.
- A managed cluster — project plan.
- Interview questions — on cloud Kubernetes.
SECTION 01Managed Kubernetes: what the cloud handles
Running Kubernetes yourself means managing the control plane, upgrades and high availability. Managed services such as Amazon EKS, Azure AKS and Google GKE run the control plane for you, while you manage node groups, networking choices, add-ons and workloads. Cloud engineers usually create these clusters with infrastructure as code and must understand which parts are their responsibility:
| Part | Managed service handles | You handle | Why it matters |
|---|---|---|---|
| Control plane | API server, etcd, availability | Version choice and upgrades timing | Less operational work |
| Nodes | Node images (optional managed groups) | Instance types, scaling, patching approach | Cost and capacity |
| Networking | Integration with cloud network | VPC design, subnets, ingress | Reachability and security |
| Add-ons and workloads | Some core add-ons | Apps, monitoring, policies | Reliability |
# cluster.yaml Create with: eksctl create cluster -f cluster.yaml
# Note: managed clusters cost money; delete after practice.
apiVersion: eksctl.io/v1alpha5
kind: ClusterConfig
metadata:
name: course-cluster
region: ap-south-1
managedNodeGroups:
- name: general
instanceType: t3.medium
minSize: 1
desiredCapacity: 2
maxSize: 3
privateNetworking: true
labels: { role: general }
#!/usr/bin/env bash
# Practise Kubernetes concepts for free on a local cluster
set -euo pipefail
kind create cluster --name practice
kubectl get nodes
kubectl create deployment web --image=nginx:1.27 --replicas=2
kubectl expose deployment web --port=80 --type=NodePort
kubectl get pods,svc
# When done:
kind delete cluster --name practice
SECTION 02How services and storage map to cloud resources
In a managed cluster, some Kubernetes objects trigger cloud resources automatically. A Service of type LoadBalancer creates a cloud load balancer; an Ingress with the right controller can create an application load balancer; a PersistentVolumeClaim with a cloud storage class creates a disk. Knowing these mappings helps you predict costs and troubleshoot when something does not appear:
| Kubernetes object | Cloud resource created | Common issue | First check |
|---|---|---|---|
| Service type LoadBalancer | Cloud load balancer | Stuck in 'pending' | Controller logs, subnet tags, permissions |
| Ingress | Application load balancer (via controller) | No address assigned | Ingress controller installed and configured |
| PersistentVolumeClaim | Block storage volume | Claim stays pending | Storage class and CSI driver |
| Pod in private subnet | Uses VPC networking | Cannot reach the internet | NAT gateway and routes |
apiVersion: v1
kind: Service
metadata:
name: web
spec:
type: LoadBalancer # the cloud creates a load balancer
selector:
app: web
ports:
- port: 80
targetPort: 8080
---
# Check with: kubectl get svc web -w
# EXTERNAL-IP stays <pending> until the cloud load balancer is ready
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: data
spec:
accessModes: ["ReadWriteOnce"]
storageClassName: gp3 # name depends on your cluster's storage classes
resources:
requests:
storage: 10Gi
---
# Debug a pending claim:
# kubectl get storageclass
# kubectl describe pvc data (read the Events section)
SECTION 03Autoscaling and workload identity
Kubernetes scales in two layers. The Horizontal Pod Autoscaler adds or removes pods based on metrics such as CPU, and a node autoscaler, such as Cluster Autoscaler or Karpenter on AWS, adds or removes cloud instances when pods cannot fit. For permissions, workload identity features let individual pods use specific cloud IAM roles, avoiding shared credentials on nodes:
| Feature | What it does | Cloud link | Good practice |
|---|---|---|---|
| Horizontal Pod Autoscaler | Scales pods on metrics | Needs metrics server | Set CPU requests on pods |
| Node autoscaling | Adds or removes nodes | Creates cloud instances | Set min and max sizes |
| Resource requests | Reserve CPU and memory | Drives node sizing | Right-size from real usage |
| Workload identity | Pod-level cloud permissions | Maps service accounts to IAM roles | Least privilege per app |
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
name: web
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: web
minReplicas: 2
maxReplicas: 10
metrics:
- type: Resource
resource:
name: cpu
target:
type: Utilization
averageUtilization: 70
# Pods must set resources.requests.cpu for this to work.
# EKS IAM Roles for Service Accounts (IRSA) example
apiVersion: v1
kind: ServiceAccount
metadata:
name: reports-reader
annotations:
eks.amazonaws.com/role-arn: arn:aws:iam::123456789012:role/reports-read-only
---
# In the Deployment's pod spec:
# serviceAccountName: reports-reader
# The pod then gets temporary credentials for that role only.
SECTION 04Managed cluster project plan
Use these templates to build a cloud Kubernetes project:
Project: Web app on a managed Kubernetes cluster
- Practise first on kind (free)
- Managed cluster created with eksctl or Terraform
- Deployment with probes and resource requests
- LoadBalancer Service or Ingress for traffic
- PersistentVolumeClaim for app data
- HPA scaling on CPU; node group min/max set
- Service account with a least-privilege cloud role
- README with diagram, cost note and teardown steps
Week 1 Kubernetes basics on kind: pods, deployments, services
Week 2 Probes, resources, ConfigMaps, Secrets
Week 3 Managed cluster: create, connect, delete
Week 4 LoadBalancer, Ingress, PVCs, troubleshooting
Week 5 HPA, node scaling, workload identity, project write-up
Before ending a practice session:
[ ] kubectl delete svc --all (removes cloud load balancers)
[ ] kubectl delete pvc --all (releases volumes)
[ ] Delete the cluster (eksctl delete cluster -f cluster.yaml)
[ ] Check for leftover load balancers and volumes in the console
[ ] Check the billing page
SECTION 05Tips for cloud engineers learning Kubernetes
These habits help you learn Kubernetes in a cloud context:
- Start locally — kind or minikube are free for basics.
- Learn the mappings — know which objects create cloud resources.
- Set resource requests — scaling and scheduling depend on them.
- Use workload identity — avoid shared node credentials.
- Always tear down — clusters and load balancers cost money.
SECTION 06Interview Q&A — cloud Kubernetes
Q1What does a managed Kubernetes service provide?
It runs and maintains the control plane, integrates with cloud networking, storage and identity, and often offers managed node groups.
Q2What happens when you create a Service of type LoadBalancer in the cloud?
The cloud integration provisions a cloud load balancer that forwards traffic to the service's pods.
Q3Why might a PersistentVolumeClaim stay pending?
No matching storage class, a missing CSI driver, quota limits or zone constraints; kubectl describe pvc shows the reason.
Q4What is the difference between pod and node autoscaling?
Pod autoscaling changes the number of pod replicas; node autoscaling changes the number of cloud instances in the cluster.
Q5How do pods get cloud permissions securely?
Through workload identity, mapping a Kubernetes service account to a specific cloud IAM role with least privilege.
SECTION 07Test yourself — cloud Kubernetes quiz
Five questions. No sign-up.
0 / 5Pick an answer to see why it is right or wrong.
SECTION 08Frequently asked questions
Do cloud engineers need Kubernetes?
Many cloud roles involve containers on managed Kubernetes. Read listings you target; Kubernetes appears often alongside cloud and Terraform skills.
Should I learn Kubernetes or a cloud provider first?
Learn cloud fundamentals, especially networking and IAM, then Kubernetes basics. Managed Kubernetes makes most sense when you know both.
Is EKS, AKS or GKE best to learn?
Choose the one matching the cloud you are learning or your target listings. Core Kubernetes concepts transfer between them.
How can I practise Kubernetes cheaply?
Use kind or minikube locally for most practice, and create a managed cluster only briefly to learn cloud integration, deleting it after.
What Kubernetes certification suits cloud engineers?
Kubernetes certifications such as CKA are respected; check the current official curriculum and focus on hands-on practice.
SECTION 09Related reads
Classroom & online · Noida
Build cloud skills that support Kubernetes
Our AWS Training Course covers IAM, networking, compute, storage and Terraform, the cloud foundations managed Kubernetes depends on.
Talk to a counsellor for current fees- Cloud networking and IAM
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