AWS Job Application Portal - Infrastructure as Code

📋 Project Overview

A scalable, cloud-based Job Application Portal deployed on AWS using Infrastructure as Code (IaC) principles. This project demonstrates the implementation of a multi-tier architecture using Terraform, CloudFormation, and Python Boto3.

Architecture Highlights

• Networking: VPC with public and private subnets across availability zones
• Compute: EC2 instances behind an Application Load Balancer with Auto Scaling
• Database: Amazon RDS (MySQL) in private subnet
• Storage: S3 bucket for resumes, company logos, and static assets
• Serverless: Lambda function triggered by S3 uploads, logging to CloudWatch
• Security: Security groups with least-privilege access controls

🏗️ Architecture Diagram

📁 Project Structure

aws-job-portal-infrastructure/
├── terraform/                    # Terraform IaC scripts
│   ├── main.tf                   # Main Terraform configuration
│   ├── variables.tf              # Variable definitions
│   ├── outputs.tf                # Output definitions
│   ├── terraform.tfvars          # Variable values (DO NOT COMMIT)
│   └── modules/
│       ├── vpc/                  # VPC module
│       └── security-groups/      # Security groups module
├── cloudformation/               # CloudFormation templates
│   ├── ec2-stack.yaml            # EC2 and ALB resources
│   ├── rds-stack.yaml            # RDS database resources
│   └── lambda-stack.yaml         # Lambda function resources
├── lambda/                       # Lambda function code
│   └── s3_upload_logger.py       # S3 event logging function
├── boto3-scripts/                # Python Boto3 scripts
│   ├── s3_operations.py          # S3 bucket and upload operations
│   ├── ec2_operations.py         # EC2 metadata and listing
│   └── lambda_operations.py      # Lambda invocation
├── web-app/                      # Web application code
│   ├── app.py                    # Flask application
│   ├── templates/                # HTML templates
│   └── static/                   # CSS, JS, images
├── docs/                         # Documentation
│   ├── architecture/             # Architecture diagrams
│   └── screenshots/              # Deployment screenshots
├── scripts/                      # Utility scripts
│   └── deploy.sh                 # Deployment automation
├── .gitignore                    # Git ignore rules
└── README.md                     # This file

🚀 Prerequisites

• AWS Account with appropriate permissions
• AWS CLI configured with credentials
• Terraform >= 1.0.0
• Python >= 3.9
• Git

⚙️ Setup Instructions

1. Clone the Repository

git clone https://github.com/YOUR_USERNAME/aws-job-portal-infrastructure.git
cd aws-job-portal-infrastructure

2. Configure AWS Credentials

aws configure
# Enter your AWS Access Key ID
# Enter your AWS Secret Access Key
# Enter default region (e.g., us-east-1)
# Enter output format (json)

3. Deploy Infrastructure with Terraform

cd terraform
terraform init
terraform plan
terraform apply

4. Deploy Resources with CloudFormation

# Deploy EC2 Stack
aws cloudformation create-stack --stack-name job-portal-ec2 \
    --template-body file://cloudformation/ec2-stack.yaml \
    --capabilities CAPABILITY_IAM

# Deploy RDS Stack
aws cloudformation create-stack --stack-name job-portal-rds \
    --template-body file://cloudformation/rds-stack.yaml

# Deploy Lambda Stack
aws cloudformation create-stack --stack-name job-portal-lambda \
    --template-body file://cloudformation/lambda-stack.yaml \
    --capabilities CAPABILITY_IAM

5. Run Boto3 Scripts

cd boto3-scripts
pip install boto3
python s3_operations.py
python ec2_operations.py
python lambda_operations.py

🔧 AWS Services Used

Service	Purpose
VPC	Network isolation with public/private subnets
EC2	Web application hosting
ALB	Load balancing and traffic distribution
Auto Scaling	Dynamic scaling based on demand
RDS (MySQL)	Relational database for job data
S3	Object storage for files
Lambda	Serverless compute for S3 event logging
CloudWatch	Monitoring and logging
IAM	Access management

📊 AWS Interaction Methods

This project demonstrates three methods of AWS interaction:

1. AWS Management Console - Visual verification of deployments
2. AWS CLI - Command-line resource management
3. Python Boto3 - Programmatic AWS operations

🧪 Testing

Verify Lambda Function

# Upload a test file to S3
aws s3 cp test-resume.pdf s3://job-portal-bucket/resumes/

# Check CloudWatch Logs
aws logs tail /aws/lambda/s3-upload-logger --follow

Verify Web Application

# Get ALB DNS name
aws elbv2 describe-load-balancers --query 'LoadBalancers[0].DNSName'
# Access the application via browser

📝 Documentation

• Architecture Design
• Deployment Guide
• Troubleshooting

🔒 Security Considerations

• RDS instance placed in private subnet (no public access)
• Security groups follow least-privilege principle
• S3 bucket has appropriate access policies
• Sensitive data stored in AWS Secrets Manager
• IAM roles with minimal required permissions

🚧 Future Improvements

• Implement CI/CD pipeline with GitHub Actions
• Add API Gateway for RESTful API
• Implement Step Functions for workflow automation
• Add CloudFront CDN for static assets
• Implement multi-region disaster recovery

👤 Author

Your Name

• Course: Cloud Computing
• Institution: UMBC
• Date: December 2025

📄 License

This project is for educational purposes as part of a cloud computing course.