Real-Time E-Commerce Analytics Platform

A production-ready serverless analytics system that processes e-commerce events in real-time, performs intelligent data aggregation, and visualizes business insights through an interactive dashboard.

🚀 Live Demo

Dashboard: https://real-time-analytics-2a54015ca-jahnavikedias-projects.vercel.app

API Endpoint: https://izob8k3keg.execute-api.us-east-1.amazonaws.com/prod/dashboard/summary

📊 Project Overview

This full-stack application demonstrates expertise in cloud architecture, real-time data processing, and modern web development. It simulates and analyzes e-commerce user behavior, providing actionable business intelligence metrics.

Key Metrics (Live Data)

679 product views processed and analyzed
169 cart additions tracked
69 purchases completed
10.16% conversion rate calculated
59.17% cart abandonment rate measured
Sub-200ms API response time achieved

🏗️ Architecture

┌─────────────────┐
│ Event Generator │ → Simulates e-commerce events
└────────┬────────┘
         ↓
┌────────────────┐
│ Kinesis Stream │ → Real-time event streaming
└────────┬───────┘
         ↓
┌─────────────────┐
│Stream Processor │ → Aggregates & processes data
└────────┬────────┘
         ↓
┌──────────────┐
│  DynamoDB    │ → Stores raw events + analytics
└──────┬───────┘
       ↓
┌─────────────┐
│ Query Lambda│ → REST API for data retrieval
└──────┬──────┘
       ↓
┌──────────────┐
│ API Gateway  │ → HTTP endpoints
└──────┬───────┘
       ↓
┌──────────────────┐
│ React Dashboard  │ → Interactive visualizations
│ (Vercel)         │
└──────────────────┘

🛠️ Technology Stack

Backend (AWS)

AWS Lambda - Serverless compute (3 functions)
Amazon DynamoDB - NoSQL database for events & analytics
Amazon Kinesis - Real-time data streaming
API Gateway - RESTful API endpoints
CloudWatch - Monitoring & logging
IAM - Security & permissions

Frontend

React - UI framework
Recharts - Data visualization
Tailwind CSS - Styling
Framer Motion - Animations
Axios - API calls
Vercel - Deployment & hosting

Development Tools

Python 3.11 - Backend logic
Node.js/npm - Frontend tooling
Git/GitHub - Version control
AWS CLI - Infrastructure management

✨ Features

Real-Time Analytics

✅ Event-driven architecture with automatic processing
✅ Sub-second latency from event generation to visualization
✅ Automatic data aggregation (hourly, daily, by category)
✅ Atomic counter updates for data consistency

Business Intelligence

📊 Conversion Rate: Measures purchase effectiveness (purchases ÷ views)
🛒 Cart Abandonment Rate: Tracks lost sales opportunities
📈 Category Performance: Identifies top-performing product categories
💰 Revenue Tracking: Monitors total sales and trends

Interactive Dashboard

🎨 Beautiful gradient UI with glass-morphism effects
📱 Fully responsive (desktop, tablet, mobile)
🔄 Auto-refresh every 30 seconds
✨ Animated number counters and smooth transitions
📊 Interactive charts with hover tooltips
🎯 Real-time data from live API

📡 API Endpoints

Base URL: https://izob8k3keg.execute-api.us-east-1.amazonaws.com/prod

1. Dashboard Summary

GET /dashboard/summary

Returns aggregated metrics with business intelligence calculations.

Response:

{
  "success": true,
  "data": {
    "total_events": 930,
    "metrics": {
      "product_view": 679,
      "add_to_cart": 169,
      "purchase": 69,
      "remove_from_cart": 13
    },
    "conversion_rate": 10.16,
    "cart_abandonment_rate": 59.17
  }
}

2. Category Breakdown

GET /dashboard/categories

Returns performance metrics by product category.

Response:

{
  "success": true,
  "data": {
    "all_time": true,
    "categories": {
      "Electronics": 350,
      "Sports": 220,
      "Home": 109
    }
  }
}

3. Specific Metric Query

GET /metrics/{metric_type}

Returns historical data for a specific metric across all dates.

Example:

GET /metrics/product_view_count

4. Metric by Date

GET /metrics/{metric_type}/{date}

Returns metric data for a specific date.

Example:

GET /metrics/purchase_count/2025-11-14

🗄️ Data Models

Raw Events Table

Table: raw-events

Partition Key: event_id (String)
Sort Key: timestamp (Number)
Purpose: Stores complete event details for audit trail

Event Types:

product_view - User views a product
add_to_cart - User adds product to cart
purchase - User completes purchase
remove_from_cart - User removes item from cart

Analytics Table

Table: analytics

Partition Key: metric_type (String)
Sort Key: time_window (String)
Purpose: Pre-aggregated metrics for fast queries

Metric Types:

product_view_count - Daily/hourly view counts
purchase_count - Daily/hourly purchase counts
category_{name} - Category-specific metrics
product_{id} - Product-specific metrics
purchase_revenue - Revenue tracking

💰 Cost Analysis

Current Monthly Cost: $0

AWS Free Tier Coverage:

Service	Free Tier	Usage	Cost
Lambda	1M requests/month	~1K/month	$0
DynamoDB	25GB storage	~0.1GB	$0
API Gateway	1M calls/month (12 months)	~500/month	$0
CloudWatch	5GB logs	~0.1GB	$0

After Free Tier (Year 2+):

Estimated: $0.50-2/month for portfolio use
Can be reduced to $0 using cleanup script

Vercel:

Free tier: Unlimited for personal projects
Cost: $0 forever

🧹 Resource Cleanup

To delete all AWS resources and return to $0/month:

cd ~/realtime-analytics-platform
./cleanup.sh

What gets deleted:

All Lambda functions
API Gateway
DynamoDB tables
Kinesis stream (if exists)
IAM roles and policies
CloudWatch alarms

What stays:

Frontend on Vercel (continues working with static data)
GitHub repository
Local code

📈 Performance Metrics

API Response Time: 150-200ms average
Event Processing Latency: <1 second
Dashboard Load Time: <2 seconds
Auto-refresh Interval: 30 seconds
Concurrent Users Supported: 1000+ (auto-scaling)

🎓 Learning Outcomes

Cloud Architecture

Designed event-driven serverless architecture
Implemented microservices pattern with Lambda
Configured cross-service IAM permissions
Set up monitoring and billing alarms

Data Engineering

Built real-time streaming pipeline with Kinesis
Designed efficient NoSQL schema with composite keys
Implemented atomic aggregations for data consistency
Optimized query patterns for sub-200ms response times

Full-Stack Development

Created RESTful API with proper HTTP methods
Built responsive React dashboard with animations
Implemented real-time data synchronization
Deployed to production with CI/CD

DevOps & Best Practices

Infrastructure as Code principles
Git version control and branching
Environment variable management
Cost optimization strategies

🔧 Local Development

Prerequisites

AWS Account with CLI configured
Node.js 16+ and npm
Python 3.11+
Git

Setup Backend

# Clone repository
git clone https://github.com/jahnavikedia/realtime-analytics-platform.git
cd realtime-analytics-platform

# Deploy AWS infrastructure
# (Follow deployment steps in documentation)

# Test API
curl https://your-api-id.execute-api.us-east-1.amazonaws.com/prod/dashboard/summary

Setup Frontend

# Navigate to frontend
cd frontend

# Install dependencies
npm install

# Start development server
npm start

# Build for production
npm run build

# Deploy to Vercel
vercel --prod

🐛 Troubleshooting

API Returns Empty Data

Issue: Dashboard shows zeros Solution: Update Query Lambda to aggregate all-time data instead of just today

CORS Errors

Issue: Browser blocks API calls Solution: Verify Access-Control-Allow-Origin: * header in Lambda response

Build Hangs Locally

Issue: npm run build gets stuck Solution: Use Vercel cloud build instead: vercel --prod

Lambda Timeout

Issue: Function execution exceeds 60 seconds Solution: Increase timeout or optimize query logic

🚧 Future Enhancements

Advanced Analytics
- User cohort analysis
- Funnel visualization
- Revenue forecasting with ML
Performance Optimization
- ElastiCache Redis for hot data
- GraphQL API for flexible queries
- WebSocket for real-time updates
Additional Features
- User authentication with Cognito
- Custom date range filtering
- Export data to CSV/PDF
- Email alerts for anomalies
Infrastructure
- Multi-region deployment
- Automated backups
- Blue-green deployments
- Load testing automation

📝 Project Structure

realtime-analytics-platform/
├── lambda/
│   ├── event-generator/         # Simulates e-commerce events
│   │   ├── lambda_function.py
│   │   └── lambda_function.zip
│   ├── stream-processor/        # Processes Kinesis stream
│   │   ├── lambda_function.py
│   │   └── lambda_function.zip
│   └── api/                     # Query API endpoints
│       ├── lambda_function.py
│       └── lambda_function.zip
├── frontend/                    # React dashboard
│   ├── public/
│   ├── src/
│   │   ├── components/
│   │   │   └── Dashboard.js
│   │   ├── services/
│   │   │   └── api.js
│   │   ├── App.js
│   │   └── index.js
│   ├── package.json
│   └── tailwind.config.js
├── infrastructure/
│   └── lambda-trust-policy.json
├── cleanup.sh                   # Resource cleanup script
├── DEPLOYMENT_INFO.md          # Deployment reference
├── PROJECT_SUMMARY.md          # Project summary
├── API_INFO.md                 # API documentation
└── README.md                   # This file

📚 Technical Documentation

Lambda Functions

1. Event Generator

Purpose: Simulates realistic e-commerce user behavior
Trigger: Manual invocation
Output: 10 events per invocation
Products: 8 different products across 3 categories
Event Distribution: 70% views, 20% cart adds, 8% purchases, 2% removals

2. Stream Processor

Purpose: Processes Kinesis events and updates analytics
Trigger: Kinesis Data Stream
Batch Size: Up to 10 records
Operations: Saves raw events, updates aggregated metrics
Concurrency: Supports parallel processing with atomic counters

3. Query API

Purpose: Exposes analytics data via REST endpoints
Trigger: API Gateway HTTP requests
Response Format: JSON with CORS headers
Features: Conversion rate calculation, all-time aggregations

Database Schema

DynamoDB Composite Keys:

Enables efficient queries by metric type and time range
Supports both specific date queries and time-series analysis
Atomic counters prevent race conditions in concurrent updates

Query Patterns:

# Get specific metric for specific date
get_item(partition_key="product_view_count", sort_key="2025-11-14")

# Get all dates for a metric (time series)
query(partition_key="product_view_count")

# Get category performance
query(partition_key="category_Electronics")

👤 Author

Jahnavi Kedia

GitHub: @jahnavikedia
Email: jahnavikedia91@gmail.com

🙏 Acknowledgments

AWS Free Tier for making cloud learning accessible
Vercel for seamless frontend deployment
React and Recharts communities for excellent documentation
Open source contributors for the libraries used

📊 Project Stats

AWS Services Used: 7
API Endpoints: 4
Events Processed: 679+
Deployment Platforms: 2 (AWS, Vercel)

Built by Jahnavi Kedia

Name		Name	Last commit message	Last commit date
Latest commit History 10 Commits
frontend		frontend
infrastructure		infrastructure
lambda		lambda
API_INFO.md		API_INFO.md
DEPLOYMENT_INFO.md		DEPLOYMENT_INFO.md
README.md		README.md
cleanup.sh		cleanup.sh

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