✅✅ # Step 1: Set Up the AWS Environment
- Set Up AWS CLI and Boto3:
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Install AWS CLI and configure it with AWS credentials.
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Install Boto3 for Python and configure it.
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✅✅ # Step 2: Prepare the MERN Application
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Containerize the MERN Application:
- Ensure the MERN application is containerized using Docker. Create a Dockerfile for each component (frontend and backend).
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Push Docker Images to Amazon ECR:
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Build Docker images for the frontend and backend.
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Create an Amazon ECR repository for each image.
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Push the Docker images to their respective ECR repositories.
- ec2-instance-frontend-service
- ec2-instance-backend-service
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✅✅ # Step 3: Version Control (Git)
✅✅ # Step 4: Continuous Integration with Jenkins
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Set Up Jenkins:
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Install Jenkins on an EC2 instance.
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Configure Jenkins with necessary plugins.
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Create Jenkins Jobs:
- Create Jenkins jobs for building and pushing Docker images to ECR.
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jenkins-push-ecr-backend
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jenkins-push-ecr-frontend
✅✅ # Step 5: Infrastructure as Code (IaC) with Boto3
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Define Infrastructure with Boto3 (Python Script):
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Use Boto3 to define the infrastructure (VPC, subnets, security groups).
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Define an Auto Scaling Group (ASG) for the backend.
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✅✅ # Step 6: Deploying Backend Services
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Deploy Backend on EC2 with ASG:
- Use Boto3 to deploy EC2 instances with the Dockerized backend application in the ASG.
✅✅ # Step 7: Set Up Networking
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Create Load Balancer:
- Set up an Elastic Load Balancer (ELB) for the backend ASG.
✅✅ # Step 8: Deploying Frontend Services
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Deploy Frontend on EC2:
- Use Boto3 to deploy EC2 instances with the Dockerized frontend application.
✅✅ # Step 9: Kubernetes (EKS) Deployment
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Create EKS Cluster:
- Use eksctl or other tools to create an Amazon EKS cluster.
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Deploy Application with Helm:
- Use Helm to package and deploy the MERN application on EKS.
