CSV Processor for Video Game Sales Data Analytics

A cloud-based serverless solution that processes CSV data containing video game sales information, generates analytics, and visualizes the results in a real-time dashboard.

🚀 Cloudfront deployment link

Dashboard Link

📊 Overview

This project demonstrates a complete serverless data processing pipeline using AWS services to analyze video game sales data. When a CSV file is uploaded to an S3 bucket, it automatically triggers an AWS Lambda function that:

1. Processes the CSV data
2. Extracts key insights like total sales, top genres, and popular platforms
3. Generates individual HTML reports for each file
4. Updates a dashboard with the latest analytics

🏗️ Architecture

• AWS Lambda: Handles the serverless processing logic
• Amazon S3: Stores input CSV files, processed results, and web assets
• Amazon CloudFront: Secures and distributes the dashboard
• AWS IAM: Manages secure access between services

🔧 Setup & Deployment

Prerequisites

• Grab sales data from Kaggle
• AWS account with appropriate permissions
• Python 3.8 or higher
• AWS CLI configured locally

Deployment Steps

1. Create S3 Buckets:

   aws s3 mb s3://your-input-bucket-name
   aws s3 mb s3://your-output-bucket-name

2. Deploy Lambda Function:

   # Create a deployment package
   zip -r function.zip lambda_function.py
   
   # Create the Lambda function
   aws lambda create-function \
     --function-name csv-processor \
     --runtime python3.8 \
     --handler lambda_function.lambda_handler \
     --role arn:aws:iam::<YOUR_ACCOUNT_ID>:role/lambda-csv-processor-role \
     --zip-file fileb://function.zip

3. Set Up S3 Trigger:

   aws lambda add-permission \
     --function-name csv-processor \
     --statement-id s3-trigger \
     --action lambda:InvokeFunction \
     --principal s3.amazonaws.com \
     --source-arn arn:aws:s3:::your-input-bucket-name

4. Upload Dashboard Assets:

   aws s3 cp dashboard/index.html s3://your-output-bucket-name/dashboard/index.html
   aws s3 cp dashboard-data.json s3://your-output-bucket-name/dashboard-data.json

5. Set Up CloudFront (Optional for Enhanced Security): Follow the steps in the AWS console to create a CloudFront distribution pointing to your S3 bucket.

🚀 Usage

1. Upload a CSV file to the input S3 bucket:

   aws s3 cp vgsales.csv s3://your-input-bucket-name/

2. Lambda processes the file automatically (no action required)

3. View the dashboard at:

   https://[your-cloudfront-domain]/
   

   Or directly from S3:

   http://your-output-bucket-name.s3-website-[region].amazonaws.com/
   

📋 CSV Format

The system expects CSV files with the following columns:

Rank, Name, Platform, Year, Genre, Publisher, NA_Sales, EU_Sales, JP_Sales, Other_Sales, Global_Sales

Example:

Rank,Name,Platform,Year,Genre,Publisher,NA_Sales,EU_Sales,JP_Sales,Other_Sales,Global_Sales
1,Wii Sports,Wii,2006,Sports,Nintendo,41.49,29.02,3.77,8.46,82.74

📊 Dashboard Features

• Summary of processed data
• Top game genres visualization
• Platform popularity analysis
• Sales trends and metrics
• Historical report access

🔒 Security Considerations

• S3 bucket access is restricted
• CloudFront distribution uses HTTPS
• IAM roles follow principle of least privilege
• No sensitive data is exposed in the dashboard

📝 Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Commit changes: git commit -m 'Add amazing feature'
4. Push to the branch: git push origin feature/amazing-feature
5. Open a Pull Request

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Note: This project is designed for demonstration purposes. For production environments, consider implementing additional security measures and error handling.