VantageFlow Cloud AWS - IoT Data Pipeline

A production-ready serverless IoT data pipeline on AWS. Modern Data Lake architecture with real-time processing and monitoring dashboard.

📊 Data Flow

flowchart TD
    A[IoT Devices] --> B[S3 Bronze<br/>Raw CSV Data]
    B --> C[Lambda Processor<br/>Clean & Validate]
    C --> D[S3 Silver<br/>Cleaned Data]
    C --> E[S3 Anomalies<br/>Suspicious Data]
    D --> F[Lambda Aggregator<br/>Group by Device]
    F --> G[S3 Gold<br/>Aggregated Stats]
    G --> H[📊 Streamlit Dashboard]
    G --> I[Power BI / Tableau]

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🚀 Quick Start

# Clone & deploy
git clone https://github.com/yourusername/vantageflow-aws.git
cd vantageflow-aws

# Deploy infrastructure
make deploy

# Test pipeline
make test

# Launch dashboard
make dashboard

🏗️ Architecture

Data Layers

Layer	Purpose	Format
Bronze	Raw IoT data	CSV files
Silver	Cleaned & validated	CSV files
Gold	Aggregated statistics	CSV files
Anomalies	Suspicious data	CSV files

AWS Services

• Amazon S3 - Data Lake storage
• AWS Lambda - Serverless processing
• CloudWatch - Monitoring & metrics
• IAM - Security & permissions
• Terraform - Infrastructure as Code

📈 Sample Output

Gold Layer Data:

device_id,total_readings,avg_value,anomaly_percentage
DEV-001,150,28.5,12.3
DEV-002,230,45.2,8.7
DEV-003,89,32.1,25.4

💰 Cost Optimization

Service	Free Tier	Production	Strategy
S3 Storage	5GB × 12 months	$0.023/GB	Lifecycle policies
Lambda	1M invocations/month	$0.20/1M	Optimized timeout
CloudWatch	5GB logs/month	$0.50/GB	7-day retention
Total	$1-3/month	$10-20/month	85% savings

📁 Project Structure

vantageflow-aws/
├── cloud-infrastructure/  # AWS Infrastructure
│   ├── terraform/        # Terraform configs
│   └── lambda/           # Lambda functions
├── data-pipeline/        # ETL scripts
├── dashboard/            # Streamlit UI
├── documentation/        # Architecture & guides
├── Makefile             # Project commands
└── README.md           # This file

🛠️ Commands

make deploy    # Deploy to AWS
make destroy   # Remove resources
make test      # Test pipeline
make monitor   # View logs
make dashboard # Launch UI
make clean     # Clean up

🔧 Tech Stack

• AWS: S3, Lambda, CloudWatch, IAM
• Languages: Python 3.11
• IaC: Terraform
• Dashboard: Streamlit, Plotly
• CI/CD: GitHub Actions

📊 Metrics & Monitoring

CloudWatch Metrics:

• RowsProcessed - Total records
• AnomalyPercentage - Anomaly rate
• ProcessingTime - Execution speed
• DevicesProcessed - Unique devices

Real-time Dashboard:

• Live pipeline status
• Data visualization
• Device analytics
• Cost monitoring

🎯 Use Cases

• IoT Data Processing - Real-time sensor data
• Data Quality - Anomaly detection & cleaning
• Business Intelligence - Ready for BI tools
• Portfolio Project - AWS skills demonstration
• Interview Prep - Technical showcase

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