🌆 Smart City Analytics Pipeline

Real-time journey analytics system tracking vehicles between Mumbai and Surat, providing insights into traffic patterns, weather conditions, and emergency responses.

🚀 Quick Start

# Clone the repository
git clone https://github.com/yourusername/smart-city-analytics.git

# Start the infrastructure
docker-compose up -d

# Run the data generator
python data_generator.py

# Launch the processing pipeline
python spark_processor.py

🎯 What Does It Do?

Our Smart City Analytics Pipeline transforms raw city data into actionable insights:

• 🚗 Vehicle Tracking: Real-time monitoring of vehicle movements
• 🌡️ Weather Analysis: Live weather condition updates
• 🚥 Traffic Monitoring: Real-time traffic pattern analysis
• 🚨 Emergency Response: Instant emergency incident detection
• 📊 Data Analytics: Comprehensive city movement insights

🏗️ Architecture

Our pipeline uses a modern data streaming architecture with:

Data Collection Layer

• 📡 Vehicle Sensors: Speed, direction, fuel type
• 📍 GPS Trackers: Real-time location data
• 📸 Traffic Cameras: Traffic monitoring
• 🌤️ Weather Stations: Environmental conditions
• 🚔 Emergency Systems: Incident reporting

Processing Layer

• 🔄 Apache Kafka: High-throughput message streaming
• ⚡ Apache Spark: Real-time data processing
• 🐳 Docker: Containerized deployment

Storage Layer

• 🗄️ AWS S3: Data lake storage
• 🏭 AWS Glue: ETL processing
• 📊 AWS Redshift: Data warehousing

Visualization Layer

• 📈 Tableau: Interactive dashboards
• 🛠️ DBeaver: SQL development

📊 Data Insights

Our system processes five key data streams:

Data Type	Description	Update Frequency	Use Case
Vehicle Data	Speed, direction, model	Real-time	Traffic optimization
GPS Data	Location tracking	Every 30 sec	Route analysis
Traffic Data	Camera snapshots	Every minute	Congestion detection
Weather Data	Temperature, humidity	Every 5 min	Environmental monitoring
Emergency Data	Incidents, accidents	Real-time	Quick response

🛠️ Setup Guide

Prerequisites

• Docker & Docker Compose
• Python 3.8+
• AWS Account
• Tableau Desktop

Configuration

1. Set Environment Variables

   # .env file
   AWS_ACCESS_KEY=your_access_key
   AWS_SECRET_KEY=your_secret_key
   KAFKA_BOOTSTRAP_SERVERS=localhost:9092

2. AWS Setup

   • Create S3 bucket
   • Configure Redshift cluster
   • Set up Glue jobs

3. Start Services

   docker-compose up -d

📈 Monitoring

Access your services:

• 🎯 Kafka Manager: localhost:9000
• 🔥 Spark Master: localhost:8080
• 📊 Tableau Dashboard: [Your Tableau Server URL]

🚀 Future Roadmap

We're working on exciting new features:

• 🤖 ML-powered traffic prediction
• 🌍 Extended city coverage
• ⚡ Real-time alerting system
• 📱 Mobile app integration
• 🔄 Automated scaling

🤝 Contributing

We love contributions! Here's how you can help:

1. 🍴 Fork the repository
2. 🌿 Create a feature branch
3. ✍️ Make your changes
4. 🔍 Test thoroughly
5. 📬 Submit a PR

📫 Need Help?

• 📝 Open an issue
• 📧 Email: [email protected]
• 💬 Join our [Slack channel]

📄 License

MIT © [Your Name]

---

Made with ❤️ for smarter cities