End-to-End Kubernetes Cluster Deployment on AWS (EKS)

Project Overview

This project focuses on deploying a Kubernetes cluster on AWS using EKS with Terraform, Helm, Docker, Prometheus, and Grafana. The deployment will include microservices with auto-scaling, load balancing, monitoring, and security best practices.

Tech Stack

• Container Orchestration: Kubernetes (EKS)
• Infrastructure as Code (IaC): Terraform
• Containerization: Docker
• Package Management: Helm
• Monitoring & Logging: Prometheus, Grafana
• Load Balancing: AWS ALB (Application Load Balancer)
• Security: IAM Roles, RBAC

Project Architecture

Components:

1. AWS EKS Cluster
• Provisioned using Terraform.
• Configured with VPC, subnets, and security groups.
• Uses managed worker nodes for running workloads.
2. Microservices Deployment
• Containerized microservices using Docker.
• Deployed using Helm charts.
• Services communicate internally within the cluster.
3. Auto Scaling and Load Balancing
• Uses Kubernetes Horizontal Pod Autoscaler (HPA).
• External traffic is handled using an AWS ALB Ingress Controller.
4. Monitoring with Prometheus & Grafana
• Prometheus collects cluster metrics.
• Grafana visualizes metrics with custom dashboards.
5. Security Implementation
• IAM roles for secure AWS service access.
• RBAC policies for role-based access control.

Step-by-Step Implementation

1. Provision AWS EKS Cluster Using Terraform

• Create a VPC, subnets, internet gateway, and route tables.
• Deploy an EKS cluster with managed worker nodes.
• Configure IAM roles for EKS and worker nodes.
• Deploy AWS ALB Ingress Controller.

Terraform Files:

• main.tf: Defines AWS resources.
• variables.tf: Stores configuration variables.
• eks.tf: Provisions the EKS cluster.
• alb.tf: Sets up the AWS ALB.

2. Build and Push Docker Images

• Containerize microservices using Docker.
• Tag and push images to Amazon Elastic Container Registry (ECR).

    docker build -t myapp:v1 .
    docker tag myapp:v1 <AWS_ACCOUNT_ID>.dkr.ecr.<REGION>.amazonaws.com/myapp:v1
    docker push <AWS_ACCOUNT_ID>.dkr.ecr.<REGION>.amazonaws.com/myapp:v1
    

3. Deploy Microservices with Helm

• Create Helm charts for microservices.
• Define Kubernetes Deployment, Service, and Ingress in Helm templates.
• Install Helm charts to deploy applications.

    helm install myapp ./helm-chart/
    

4. Configure Auto Scaling and Load Balancing

• Implement Horizontal Pod Autoscaler (HPA).

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: myapp-hpa
spec:
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      targetAverageUtilization: 50

• Apply HPA:

    kubectl apply -f hpa.yaml
    

• Deploy AWS ALB Ingress Controller:

helm upgrade --install aws-load-balancer-controller eks/aws-load-balancer-controller -n kube-system

5. Set Up Monitoring with Prometheus and Grafana

• Install Prometheus and Grafana using Helm:

    helm install prometheus prometheus-community/kube-prometheus-stack
    helm install grafana grafana/grafana
    

• Access Grafana dashboard:

kubectl port-forward svc/grafana 3000:80 -n monitoring

6. Secure Cluster with IAM and RBAC

• Define IAM roles for accessing AWS services securely.
• Implement RBAC policies to restrict user permissions.

Example RBAC Policy:

apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
  namespace: default
  name: app-role
rules:
- apiGroups: [""]
  resources: ["pods"]
  verbs: ["get", "list"]

Apply the policy:

    kubectl apply -f rbac.yaml
    

Project Deliverables

• Terraform files for provisioning EKS.
• Helm charts for deploying microservices.
• Docker images pushed to ECR.
• HPA configuration for auto-scaling.
• ALB Ingress Controller for traffic management.
• Prometheus & Grafana monitoring dashboards.
• RBAC policies and IAM roles for security.

Outcome

This project ensures a scalable, secure, and fully monitored Kubernetes environment on AWS. It follows best practices in automation, security, and observability to efficiently manage microservices in production.

Next Steps

• Implement logging with Fluentd & Elasticsearch.
• Configure service mesh (Istio or Linkerd).
• Add CI/CD automation using Jenkins or GitHub Actions.

This project is ready for hands-on implementation! 🚀