Warrior, this is the final chapter of your Kubernetes course.
We’ll now combine almost everything you learned into one realistic production architecture:
Next.js → Node.js API → PostgreSQL + Redis → Kubernetes → Ingress → HPA → Monitoring → Logging → CI/CD → Argo CD
Let's build a production-style application called:
Architecture:
INTERNET
│
▼
┌───────────────┐
│ DNS / HTTPS │
└───────┬───────┘
│
▼
┌───────────────┐
│ Ingress │
└───────┬───────┘
│
┌─────────────┴─────────────┐
│ │
▼ ▼
┌─────────────┐ ┌─────────────┐
│ Next.js │ │ Node.js │
│ Frontend │ │ API │
└─────────────┘ └──────┬──────┘
│
┌───────────────┼───────────────┐
│ │ │
▼ ▼ ▼
PostgreSQL Redis AI Worker
Database Cache / RAG
│ │
▼ ▼
Persistent Cache
Storage DataAnd around the application:
┌──────────────────────────────┐
│ Kubernetes │
│ │
│ ┌────────────────────────┐ │
│ │ Deployments │ │
│ │ Services │ │
│ │ Ingress │ │
│ │ HPA │ │
│ │ ConfigMaps │ │
│ │ Secrets │ │
│ │ RBAC │ │
│ │ NetworkPolicies │ │
│ │ Jobs / CronJobs │ │
│ └────────────────────────┘ │
│ │
└──────────────────────────────┘We'll use:
| Layer | Technology |
|---|---|
| Frontend | Next.js |
| Backend | Node.js + Express |
| Database | PostgreSQL |
| Cache | Redis |
| AI Worker | Python/Node.js |
| Container | Docker |
| Orchestration | Kubernetes |
| Package Manager | Helm |
| Configuration | Kustomize |
| Ingress | NGINX / cloud ingress |
| Monitoring | Prometheus |
| Dashboards | Grafana |
| Logging | Fluent Bit + Loki/ELK |
| CI | GitHub Actions |
| Registry | ECR |
| GitOps | Argo CD |
| Cloud | AWS EKS |
A realistic repository could look like:
kube-task/
│
├── frontend/
│ ├── app/
│ ├── package.json
│ ├── Dockerfile
│ └── ...
│
├── backend/
│ ├── src/
│ ├── package.json
│ ├── Dockerfile
│ └── ...
│
├── worker/
│ ├── src/
│ ├── requirements.txt
│ └── Dockerfile
│
├── k8s/
│ ├── namespace.yaml
│ │
│ ├── frontend/
│ │ ├── deployment.yaml
│ │ └── service.yaml
│ │
│ ├── backend/
│ │ ├── deployment.yaml
│ │ ├── service.yaml
│ │ ├── hpa.yaml
│ │ └── serviceaccount.yaml
│ │
│ ├── postgres/
│ │ ├── deployment.yaml
│ │ ├── service.yaml
│ │ └── pvc.yaml
│ │
│ ├── redis/
│ │ ├── deployment.yaml
│ │ └── service.yaml
│ │
│ ├── ingress.yaml
│ ├── configmap.yaml
│ ├── secret.yaml
│ ├── network-policy.yaml
│ └── migration-job.yaml
│
├── helm/
│ └── kube-task/
│
├── .github/
│ └── workflows/
│ └── deploy.yml
│
└── README.mdFROM node:22-alpine
WORKDIR /app
COPY package*.json ./
RUN npm ci --omit=dev
COPY . .
EXPOSE 3000
CMD ["node", "src/server.js"]Build:
docker build -t kube-task-api:1.0 .Run:
docker run -p 3000:3000 kube-task-api:1.0For production, Next.js can use standalone output.
next.config.js:
module.exports = {
output: "standalone"
};Dockerfile:
FROM node:22-alpine AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci
COPY . .
RUN npm run build
FROM node:22-alpine AS runner
WORKDIR /app
ENV NODE_ENV=production
COPY --from=builder /app/public ./public
COPY --from=builder /app/.next/standalone ./
COPY --from=builder /app/.next/static ./.next/static
EXPOSE 3000
CMD ["node", "server.js"]This gives us a smaller production runtime than shipping the entire development environment.
Never depend on:
latestPrefer:
kube-task-api:1.0.0or:
kube-task-api:8f31c2awhere:
8f31c2ais the Git commit SHA.
Even better in production:
kube-task-api@sha256:...because the digest identifies an immutable image.
Create:
apiVersion: v1
kind: Namespace
metadata:
name: kube-taskApply:
kubectl apply -f namespace.yamlCheck:
kubectl get namespacesOur non-sensitive configuration:
apiVersion: v1
kind: ConfigMap
metadata:
name: kube-task-config
namespace: kube-task
data:
NODE_ENV: "production"
PORT: "3000"
REDIS_HOST: "redis"
REDIS_PORT: "6379"
POSTGRES_HOST: "postgres"
POSTGRES_PORT: "5432"Remember:
ConfigMap
↓
Non-sensitive configurationSensitive values:
apiVersion: v1
kind: Secret
metadata:
name: kube-task-secret
namespace: kube-task
type: Opaque
stringData:
POSTGRES_USER: "appuser"
POSTGRES_PASSWORD: "strong-password"
POSTGRES_DB: "kube_task"
JWT_SECRET: "super-secret-value"⚠️ Don't commit real production secrets like this to Git.
In a real EKS environment, you'd typically use something such as:
AWS Secrets Manager
↓
External Secrets / secret integration
↓
Kubernetes Secret
↓
PodFor learning, we can run PostgreSQL inside Kubernetes.
apiVersion: apps/v1
kind: Deployment
metadata:
name: postgres
namespace: kube-task
spec:
replicas: 1
selector:
matchLabels:
app: postgres
template:
metadata:
labels:
app: postgres
spec:
containers:
- name: postgres
image: postgres:16
ports:
- containerPort: 5432
env:
- name: POSTGRES_USER
valueFrom:
secretKeyRef:
name: kube-task-secret
key: POSTGRES_USER
- name: POSTGRES_PASSWORD
valueFrom:
secretKeyRef:
name: kube-task-secret
key: POSTGRES_PASSWORD
- name: POSTGRES_DB
valueFrom:
secretKeyRef:
name: kube-task-secret
key: POSTGRES_DB
volumeMounts:
- name: postgres-storage
mountPath: /var/lib/postgresql/data
volumes:
- name: postgres-storage
persistentVolumeClaim:
claimName: postgres-pvcapiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: postgres-pvc
namespace: kube-task
spec:
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 20GiArchitecture:
PostgreSQL Pod
│
▼
PVC 20Gi
│
▼
Persistent Volume
│
▼
Cloud DiskapiVersion: v1
kind: Service
metadata:
name: postgres
namespace: kube-task
spec:
selector:
app: postgres
ports:
- port: 5432
targetPort: 5432Now backend can connect using:
postgres:5432Not:
localhost:5432This is an extremely important Kubernetes concept.
Inside backend:
localhostmeans:
the backend container/Pod's own network namespace.
Whereas:
postgresmeans:
PostgreSQL Kubernetes Service.
Redis can be exposed internally:
apiVersion: apps/v1
kind: Deployment
metadata:
name: redis
namespace: kube-task
spec:
replicas: 1
selector:
matchLabels:
app: redis
template:
metadata:
labels:
app: redis
spec:
containers:
- name: redis
image: redis:7-alpine
ports:
- containerPort: 6379Service:
apiVersion: v1
kind: Service
metadata:
name: redis
namespace: kube-task
spec:
selector:
app: redis
ports:
- port: 6379
targetPort: 6379Backend:
REDIS_HOST=redis
REDIS_PORT=6379Now the important part.
apiVersion: apps/v1
kind: Deployment
metadata:
name: backend
namespace: kube-task
spec:
replicas: 3
strategy:
type: RollingUpdate
rollingUpdate:
maxUnavailable: 1
maxSurge: 1
selector:
matchLabels:
app: backend
template:
metadata:
labels:
app: backend
spec:
serviceAccountName: backend-sa
containers:
- name: backend
image: 123456789.dkr.ecr.ap-south-1.amazonaws.com/kube-task-api:1.0.0
ports:
- containerPort: 3000
envFrom:
- configMapRef:
name: kube-task-config
- secretRef:
name: kube-task-secret
resources:
requests:
cpu: "250m"
memory: "256Mi"
limits:
cpu: "500m"
memory: "512Mi"
startupProbe:
httpGet:
path: /health
port: 3000
failureThreshold: 30
periodSeconds: 5
readinessProbe:
httpGet:
path: /ready
port: 3000
periodSeconds: 10
livenessProbe:
httpGet:
path: /health
port: 3000
periodSeconds: 10Look at how many concepts are now coming together:
Deployment
+
ServiceAccount
+
ConfigMap
+
Secret
+
Resources
+
Startup Probe
+
Readiness Probe
+
Liveness Probe
+
Rolling UpdateThis is why Kubernetes becomes powerful when you combine the individual concepts.
apiVersion: v1
kind: Service
metadata:
name: backend
namespace: kube-task
spec:
selector:
app: backend
ports:
- port: 80
targetPort: 3000
type: ClusterIPTherefore:
Backend Service :80
↓
Backend Pod :3000Frontend can call:
http://backendNow suppose traffic increases.
We want:
3 Pods
↓
4 Pods
↓
5 Pods
↓
...
↓
10 PodsHPA:
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
name: backend-hpa
namespace: kube-task
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: backend
minReplicas: 3
maxReplicas: 10
metrics:
- type: Resource
resource:
name: cpu
target:
type: Utilization
averageUtilization: 70Architecture:
Traffic
│
▼
Backend Pods
│
▼
CPU increases
│
▼
HPA
│
▼
More PodsapiVersion: apps/v1
kind: Deployment
metadata:
name: frontend
namespace: kube-task
spec:
replicas: 3
selector:
matchLabels:
app: frontend
template:
metadata:
labels:
app: frontend
spec:
containers:
- name: frontend
image: 123456789.dkr.ecr.ap-south-1.amazonaws.com/kube-task-frontend:1.0.0
ports:
- containerPort: 3000
resources:
requests:
cpu: "200m"
memory: "256Mi"
limits:
cpu: "500m"
memory: "512Mi"
readinessProbe:
httpGet:
path: /
port: 3000
periodSeconds: 10Service:
apiVersion: v1
kind: Service
metadata:
name: frontend
namespace: kube-task
spec:
selector:
app: frontend
ports:
- port: 80
targetPort: 3000Now expose our application.
Imagine:
app.kubetask.com
api.kubetask.comIngress:
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
name: kube-task-ingress
namespace: kube-task
spec:
ingressClassName: nginx
rules:
- host: app.kubetask.com
http:
paths:
- path: /
pathType: Prefix
backend:
service:
name: frontend
port:
number: 80
- host: api.kubetask.com
http:
paths:
- path: /
pathType: Prefix
backend:
service:
name: backend
port:
number: 80Traffic:
app.kubetask.com
│
▼
Ingress
│
▼
frontend
│
▼
Next.js PodsAnd:
api.kubetask.com
│
▼
Ingress
│
▼
backend
│
▼
Node.js PodsCreate a ServiceAccount:
apiVersion: v1
kind: ServiceAccount
metadata:
name: backend-sa
namespace: kube-taskSuppose backend needs to read ConfigMaps.
Role:
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
name: backend-config-reader
namespace: kube-task
rules:
- apiGroups: [""]
resources:
- configmaps
verbs:
- get
- list
- watchRoleBinding:
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
name: backend-config-reader-binding
namespace: kube-task
subjects:
- kind: ServiceAccount
name: backend-sa
namespace: kube-task
roleRef:
kind: Role
name: backend-config-reader
apiGroup: rbac.authorization.k8s.ioRemember:
ServiceAccount
↓
RoleBinding
↓
Role
↓
PermissionsLet's create a security architecture:
Internet
│
▼
Frontend
│
▼
Backend
│
┌─┴─────────┐
▼ ▼
Redis PostgreSQLWe don't want:
Frontend → PostgreSQL
Frontend → Redis
Random Pod → PostgreSQLExample PostgreSQL policy:
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
name: postgres-policy
namespace: kube-task
spec:
podSelector:
matchLabels:
app: postgres
policyTypes:
- Ingress
ingress:
- from:
- podSelector:
matchLabels:
app: backend
ports:
- protocol: TCP
port: 5432Now:
Backend ───────────► PostgreSQL ✅
Frontend ──────────► PostgreSQL ❌
Random Pod ────────► PostgreSQL ❌Before deploying a new version:
Application
│
▼
Database migration
│
▼
New applicationKubernetes Job:
apiVersion: batch/v1
kind: Job
metadata:
name: db-migration
namespace: kube-task
spec:
backoffLimit: 3
template:
spec:
restartPolicy: Never
containers:
- name: migration
image: 123456789.dkr.ecr.ap-south-1.amazonaws.com/kube-task-api:1.0.0
command:
- npm
- run
- migrate
envFrom:
- configMapRef:
name: kube-task-config
- secretRef:
name: kube-task-secretRun:
kubectl apply -f migration-job.yamlCheck:
kubectl get jobsSuppose we want daily cleanup:
Every day at 2 AM
↓
Cleanup expired sessions
↓
JobapiVersion: batch/v1
kind: CronJob
metadata:
name: cleanup
namespace: kube-task
spec:
schedule: "0 2 * * *"
concurrencyPolicy: Forbid
jobTemplate:
spec:
template:
spec:
restartPolicy: Never
containers:
- name: cleanup
image: kube-task-api:1.0.0
command:
- npm
- run
- cleanupNow production needs visibility.
Architecture:
Applications
│
▼
/metrics
│
▼
Prometheus
│
▼
GrafanaExample metrics:
http_requests_total
http_request_duration_seconds
nodejs_heap_size_used_bytes
process_cpu_seconds_totalFor Kubernetes:
Pod CPU
Pod Memory
Pod Restarts
Deployment replicas
Node capacity
HPA statusA useful dashboard could show:
┌─────────────────────────────────────────────┐
│ KUBE-TASK PROD │
├──────────────┬──────────────┬───────────────┤
│ Requests/sec │ Error Rate │ P95 Latency │
│ 1,240 │ 0.3% │ 180 ms │
├──────────────┼──────────────┼───────────────┤
│ Backend Pods │ CPU │ Memory │
│ 6/10 │ 63% │ 71% │
├──────────────┴──────────────┴───────────────┤
│ Request Rate Graph │
│ ╱╲ ╱╲ │
│ ╱╲ ╱ ╲ ╱╲ ╱ ╲ │
└─────────────────────────────────────────────┘Golden Signals:
Latency
Traffic
Errors
SaturationInstead of:
kubectl logsfor every Pod manually:
Pod
│
▼
stdout
│
▼
Node
│
▼
Fluent Bit
│
▼
Loki / Elasticsearch
│
▼
Grafana / KibanaNow if a Pod disappears, its logs can still remain in centralized storage depending on retention.
Now connect GitHub.
Developer
│
▼
Git Push
│
▼
GitHub
│
▼
GitHub Actions
│
┌──┼───────────────┐
│ │ │
▼ ▼ ▼
Test Lint Security Scan
│
▼
Docker Build
│
▼
ECRThen GitOps:
Deployment Git
│
▼
Argo CD
│
▼
KubernetesSimplified pipeline:
name: Build
on:
push:
branches:
- main
jobs:
build:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v4
- name: Setup Node
uses: actions/setup-node@v4
with:
node-version: 22
- name: Install
run: npm ci
- name: Test
run: npm test
- name: Build Docker image
run: |
docker build \
-t kube-task-api:${{ github.sha }} \
./backendThen:
docker pushto ECR.
Deployment repository:
kube-task-config/
│
├── base/
│ ├── deployment.yaml
│ ├── service.yaml
│ └── kustomization.yaml
│
└── overlays/
├── dev/
├── staging/
└── production/Production overlay:
images:
- name: kube-task-api
newName: 123456789.dkr.ecr.ap-south-1.amazonaws.com/kube-task-api
newTag: "8f31c2a"Developer changes Git:
image: 8f31c2aArgo sees:
Git desired state
≠
Cluster stateThen:
Argo CD
↓
Sync
↓
KubernetesThis is one of the most important diagrams in your entire Kubernetes course.
DEVELOPER
│
▼
Git Push
│
▼
┌───────────────┐
│ GitHub Actions│
└───────┬───────┘
│
┌─────────┼──────────┐
▼ ▼ ▼
Test Lint Security
│
▼
Docker Build
│
▼
Container Image
│
▼
ECR
│
│
▼
Deployment Git Repo
│
▼
Argo CD
│
▼
Kubernetes API
│
▼
Deployment
│
▼
ReplicaSet
│
▼
Pods
│
┌────┴────┐
▼ ▼
Readiness Liveness
│
▼
Service
│
▼
Ingress
│
▼
UserWhen a user opens:
https://app.kubetask.comthe flow becomes:
Browser
│
▼
DNS
│
▼
AWS Load Balancer
│
▼
Ingress Controller
│
▼
Frontend Service
│
▼
Next.js Pod
│
▼
Backend Service
│
▼
Node.js Pod
│
├───────────────┐
▼ ▼
PostgreSQL RedisSuppose:
100 requests/secbecomes:
10,000 requests/secThen:
Traffic ↑
│
▼
CPU ↑
│
▼
HPA
│
▼
Pods 3 → 10
│
▼
SchedulerIf there isn't enough node capacity:
Pods
│
▼
Pending
│
▼
Cluster Autoscaler
│
▼
New Node
│
▼
Scheduler
│
▼
Pod RunningThis is the complete relationship:
HPA
│
│ scales
▼
Pods
│
│ may require
▼
Cluster Autoscaler
│
│ scales
▼
NodesSuppose:
Backend Pod #2
↓
CrashDeployment/ReplicaSet notices:
Desired = 3
Current = 2Therefore:
ReplicaSet
↓
Create replacement PodThen:
Startup Probe
↓
Readiness Probe
↓
Service adds Pod
↓
TrafficThat's Kubernetes reconciliation in action.
Suppose:
Node 2 💀Pods disappear.
Kubernetes sees:
Desired Pods = 6
Available = 4Scheduler can place replacement Pods on healthy nodes.
If the cluster needs additional capacity:
Pending Pods
↓
Cluster Autoscaler
↓
New Node
↓
Pods scheduledA stronger architecture:
Internet
│
▼
WAF
│
▼
Load Balancer
│
▼
Ingress
│
▼
Frontend
│
▼
Backend
/ \
/ \
▼ ▼
PostgreSQL RedisSecurity layers:
WAF
│
▼
TLS
│
▼
Ingress
│
▼
NetworkPolicy
│
▼
ServiceAccount
│
▼
RBAC
│
▼
Secrets
│
▼
Container SecurityAnd:
AWS IAM
+
Kubernetes RBAC
+
NetworkPolicy
+
Security Groupsare different security layers.
Now let's move from local Kubernetes to cloud.
Route 53
│
▼
AWS Load Balancer
│
▼
Ingress
│
┌────────────┴────────────┐
▼ ▼
Next.js Node.js
Pods Pods
│
┌──────────┴──────────┐
▼ ▼
RDS ElastiCache
PostgreSQL RedisAround it:
┌─────────────────┐
│ EKS │
│ │
│ Node Group │
│ Node Group │
│ Node Group │
└─────────────────┘For production, you would commonly consider managed services for critical stateful components rather than putting your primary PostgreSQL database directly in a Deployment.
For example:
EKS
│
├── Next.js
├── Node.js
├── Workers
│
├── RDS PostgreSQL
└── ElastiCache RedisAt this point, you should be able to visualize Kubernetes like this:
┌───────────────┐
│ INGRESS │
└───────┬───────┘
│
▼
┌───────────────┐
│ SERVICE │
└───────┬───────┘
│
┌────────────┼────────────┐
▼ ▼ ▼
Pod Pod Pod
│ │ │
└────────────┼────────────┘
│
Deployment
│
ReplicaSetConfiguration:
ConfigMap
+
Secret
↓
PodStorage:
Pod
↓
PVC
↓
PV
↓
StorageScaling:
HPA
↓
Pods
Cluster Autoscaler
↓
NodesSecurity:
ServiceAccount
↓
RBAC
NetworkPolicy
↓
Network trafficObservability:
Logs → Loki/ELK
Metrics → Prometheus
Dashboard → GrafanaDeployment:
Git
↓
CI/CD
↓
Container Registry
↓
Argo CD
↓
Kubernetes| Concept | Purpose |
|---|---|
| Cluster | Complete Kubernetes environment |
| Node | Machine running workloads |
| Pod | Smallest deployable unit |
| Container | Application runtime |
| Deployment | Manages stateless Pods |
| ReplicaSet | Maintains replica count |
| Service | Stable networking |
| Ingress | HTTP/HTTPS routing |
| ConfigMap | Non-sensitive configuration |
| Secret | Sensitive configuration |
| Volume | Storage abstraction |
| PVC | Request for storage |
| StatefulSet | Stateful workloads |
| Job | One-time task |
| CronJob | Scheduled task |
| HPA | Scales Pods |
| Cluster Autoscaler | Scales Nodes |
| RBAC | Kubernetes API permissions |
| ServiceAccount | Workload identity |
| NetworkPolicy | Pod network restrictions |
| Helm | Package/release management |
| Kustomize | YAML customization |
| Prometheus | Metrics |
| Grafana | Visualization |
| Fluent Bit | Log collection |
| Argo CD | GitOps deployment |
Don't try to build the entire production system on day one.
Build it in stages.
Next.js
+
Node.jsRun in Kubernetes.
Add:
Service
IngressAdd:
PostgreSQL
RedisAdd:
ConfigMap
Secret
PVCAdd:
Readiness
Liveness
StartupAdd:
Resources
HPAAdd:
RBAC
ServiceAccount
NetworkPolicyAdd:
Jobs
CronJobsPackage:
HelmConfigure:
KustomizeCI/CD:
GitHub Actions
↓
Docker
↓
ECRGitOps:
Argo CDObservability:
Prometheus
Grafana
Loki/ELKCloud:
AWS EKSBefore calling an application production-ready:
You should now be able to answer these.
1. What is Kubernetes?
Container orchestration platform that automates deployment, scaling, networking and management of containerized workloads.
2. What is a Pod?
Smallest deployable Kubernetes unit containing one or more containers.
3. Deployment vs Pod?
Deployment manages Pods and provides rollout/replica management.
4. Why Service?
Pods are ephemeral; Service provides stable networking and service discovery.
5. HPA vs Cluster Autoscaler?
HPA → Pods
CA → Nodes6. ConfigMap vs Secret?
ConfigMap → normal configuration
Secret → sensitive configuration7. Readiness vs Liveness?
Readiness → receive traffic?
Liveness → should restart?8. Deployment vs StatefulSet?
Deployment → stateless
StatefulSet → stateful workloads9. How does Kubernetes self-heal?
Controllers continuously compare desired state with actual state and take corrective actions.
10. How does HPA work?
It observes metrics and adjusts the replica count of a scalable workload.
11. How does Cluster Autoscaler work?
It adjusts node capacity based largely on unschedulable Pods and whether nodes can be safely removed.
12. What is GitOps?
Git stores desired deployment state and a controller such as Argo CD reconciles the cluster toward that state.
13. How would you deploy a Node.js application to EKS?
A strong answer:
Git
↓
GitHub Actions
↓
Tests
↓
Docker build
↓
ECR
↓
Deployment configuration
↓
Argo CD
↓
EKS
↓
Deployment
↓
Pods
↓
Service
↓
Ingress
↓
Users14. How would you troubleshoot a production Pod?
kubectl get pod
↓
kubectl describe pod
↓
kubectl logs
↓
kubectl logs --previous
↓
Events
↓
Service
↓
EndpointSlices
↓
Network/DNS
↓
Node/resources15. How would you secure Kubernetes?
RBAC
+
ServiceAccounts
+
Secrets management
+
NetworkPolicies
+
Pod security
+
Non-root containers
+
Least privilege
+
Cloud IAM/workload identityWarrior, if you remember only one diagram from the entire course, remember this:
USERS
│
▼
DNS
│
▼
Load Balancer
│
▼
INGRESS
│
┌────────┴────────┐
▼ ▼
FRONTEND BACKEND
SERVICE SERVICE
│ │
┌──┴──┐ ┌──┴──┐
▼ ▼ ▼ ▼
Pod Pod Pod Pod
│
┌──────┴──────┐
▼ ▼
PostgreSQL Redis
│
▼
PVC
┌─────────────────────────────────┐
│ KUBERNETES │
│ │
│ HPA → Pods │
│ CA → Nodes │
│ RBAC → API permissions │
│ NetworkPolicy → Network access │
│ Probes → Health │
│ Prometheus → Metrics │
│ Grafana → Dashboards │
│ Logs → Centralized logging │
└─────────────────────────────────┘
Git
│
▼
GitHub Actions
│
├── Test
├── Security
└── Docker
│
▼
ECR
│
▼
Deployment Git
│
▼
Argo CD
│
▼
KubernetesYou have now covered the full path:
Kubernetes Basics
↓
Docker
↓
Architecture
↓
kubectl
↓
Pods
↓
Deployments
↓
Services
↓
Namespaces
↓
ConfigMaps / Secrets
↓
Storage
↓
Networking
↓
Ingress
↓
Resources
↓
Probes
↓
HPA
↓
Cluster Autoscaler
↓
Scheduling
↓
Jobs / CronJobs
↓
Security / RBAC
↓
Network Policies
↓
Logging
↓
Monitoring
↓
Troubleshooting
↓
Rollouts / Rollbacks
↓
Helm
↓
Kustomize
↓
Local Kubernetes
↓
EKS / GKE / AKS
↓
CI/CD
↓
GitOps / Argo CD
↓
🔥 Production ProjectThe next step should be implementation, not another theory chapter: take the KubeTask architecture above and build it locally from zero, command-by-command, starting with Docker → Next.js/Node.js → Kubernetes → PostgreSQL/Redis → Ingress → HPA → monitoring, and finally deploy the same architecture to EKS.
Mark it as complete to track your mastery.