Large-Scale Data Analytics Demo

A comprehensive end-to-end data analytics pipeline showcasing distributed data processing, data warehousing, and machine learning on large datasets. This production-ready demo demonstrates enterprise-level data engineering and machine learning practices.

🎯 Overview

This repository demonstrates a complete big data analytics workflow using modern tools and best practices. The pipeline processes multi-million row datasets, performs distributed data cleaning and feature engineering, leverages cloud data warehousing, and builds predictive models to generate actionable business insights.

🛠 Tech Stack

• PySpark 3.4.1 - Distributed data processing and feature engineering
• Amazon Redshift - Cloud data warehousing and analytical queries
• Scikit-learn 1.3.0 - Machine learning model development
• Jupyter Notebooks - Interactive analysis and documentation
• Python 3.10+ - Core programming language
• Docker & Docker Compose - Containerization for reproducible environments
• MLflow - Experiment tracking and model registry
• Redis - Caching and session storage
• PostgreSQL - Local development database
• Grafana & Prometheus - Monitoring and metrics

📊 Dataset

This demo uses a synthetic e-commerce dataset generated for demonstration purposes, containing:

• 500K+ transaction records
• 50K customers with demographic information
• 1K products across multiple categories
• Multi-year transaction history (2021-2023)
• Customer behavior patterns and seasonal trends

🏗 Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Raw Data      │───▶│   PySpark       │───▶│   Processed     │
│   (Synthetic)   │    │   Processing    │    │   Features      │
└─────────────────┘    └─────────────────┘    └─────────────────┘
                                │
                                ▼
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Business      │◀───│   ML Models     │◀───│   Amazon        │
│   Insights      │    │   (Scikit-learn)│    │   Redshift      │
└─────────────────┘    └─────────────────┘    └─────────────────┘
                                │
                                ▼
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   MLflow        │    │   Docker        │    │   Monitoring    │
│   Tracking      │    │   Services      │    │   & Alerts      │
└─────────────────┘    └─────────────────┘    └─────────────────┘

📁 Project Structure

data-science-demo/
├── data/
│   ├── raw/                    # Raw datasets from Kaggle
│   └── processed/              # Cleaned and engineered features
├── notebooks/
│   ├── 01_data_exploration.ipynb
│   ├── 02_data_processing.ipynb
│   ├── 03_feature_engineering.ipynb
│   ├── 04_ml_modeling.ipynb
│   └── 05_business_insights.ipynb
├── src/
│   ├── data_processing/
│   │   ├── __init__.py
│   │   ├── spark_processor.py  # PySpark data processing
│   │   ├── feature_engineer.py # Feature engineering pipeline
│   │   └── data_validator.py   # Data quality checks
│   └── ml_models/
│       ├── __init__.py
│       ├── base_model.py       # Base model class
│       ├── logistic_model.py   # Logistic regression
│       └── random_forest.py    # Random forest classifier
├── sql/
│   ├── create_tables.sql       # Redshift table schemas
│   ├── data_warehouse_etl.sql  # ETL procedures
│   └── analytical_queries.sql  # Business intelligence queries
├── config/
│   ├── spark_config.py         # Spark configuration
│   ├── redshift_config.py      # Redshift connection settings
│   └── model_config.yaml       # ML model hyperparameters
├── tests/
│   ├── test_data_processing.py
│   └── test_ml_models.py
├── requirements.txt
├── setup.py
├── Dockerfile
└── docker-compose.yml

🚀 Getting Started

Prerequisites

• Python 3.8+
• Java 8 (for PySpark)
• Docker & Docker Compose
• AWS CLI (for Redshift access)

Installation

1. Clone the repository

git clone <repository-url>
cd data-science-demo

2. Set up virtual environment

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Configure environment variables

cp config/.env.example config/.env
# Edit config/.env with your AWS credentials and Redshift details

5. Start services with Docker

docker-compose up -d

Quick Start

1. Download the dataset

python src/data_processing/download_data.py

2. Run the complete pipeline

python src/main.py --pipeline full

3. Launch Jupyter notebooks

jupyter notebook notebooks/

📈 Pipeline Stages

1. Data Ingestion & Exploration

• Generate or load datasets with comprehensive profiling
• Perform initial data quality assessment and validation
• Generate descriptive statistics and visualizations
• Create automated data profiling reports

2. Distributed Data Processing (PySpark)

• Data Cleaning: Handle missing values, outliers, and inconsistencies
• Feature Engineering: Create time-based, aggregation, and interaction features
• Data Validation: Implement comprehensive data quality checks with scoring
• Partitioning: Optimize data storage for analytical queries

3. Data Warehousing (Redshift/PostgreSQL)

• Schema Design: Create star schema with fact and dimension tables
• ETL Process: Load processed data with proper data types and constraints
• Query Optimization: Implement analytical queries with performance tuning
• ML Feature Store: Create dedicated tables for model features and predictions

4. Machine Learning Pipeline

• Feature Selection: Statistical, correlation-based, and recursive feature elimination
• Model Training: Logistic regression and random forest with hyperparameter tuning
• Model Evaluation: Cross-validation, holdout testing, and comprehensive metrics
• Model Persistence: Save models with versioning and metadata tracking

5. Business Intelligence & Reporting

• Customer Segmentation: RFM analysis and behavioral clustering
• Predictive Analytics: Churn prediction and customer lifetime value
• Interactive Dashboards: Jupyter notebooks with rich visualizations
• Automated Reporting: Generate comprehensive pipeline reports

🎯 Key Features Demonstrated

• Enterprise Data Pipeline: Production-ready architecture with proper error handling
• Distributed Computing: PySpark optimization for large-scale data processing
• Feature Engineering: Advanced techniques including time-series and aggregations
• Model Management: MLflow integration for experiment tracking
• Data Quality: Comprehensive validation with automated scoring
• Containerization: Docker services for development and deployment
• Monitoring: Built-in logging, metrics collection, and health checks

📊 Example Model Performance

Model	Accuracy	Precision	Recall	F1-Score	Training Time
Logistic Regression	87.3%	85.1%	89.2%	87.1%	2.3s
Random Forest	91.7%	90.4%	92.8%	91.6%	15.7s

Note: Performance metrics will vary based on the synthetic dataset generated

🔧 Configuration

Development Environment

• Spark: Local mode with 4 cores, 4GB memory
• Database: PostgreSQL 15 for local development
• Storage: Local filesystem with Parquet format
• Monitoring: Jupyter Lab, Spark UI, and custom dashboards

Production-Ready Features

• Scalable Architecture: Configurable for cluster deployment
• Security: IAM authentication, SSL encryption, VPC support
• Monitoring: Prometheus metrics, Grafana dashboards, alerting
• CI/CD Ready: Docker images, automated testing, deployment scripts

📚 Notebooks Overview

1. Data Exploration - Initial dataset analysis and profiling
2. Data Processing - PySpark cleaning and transformation pipeline
3. Feature Engineering - Advanced feature creation and selection
4. ML Modeling - Model training, tuning, and evaluation
5. Business Insights - Visualization and interpretation of results

🧪 Testing

Run the test suite to ensure code quality:

pytest tests/ -v --cov=src/

📝 Documentation

Detailed documentation is available in the docs/ directory:

• Data Processing Guide
• Model Development Guide
• Redshift Setup Guide
• API Reference

🤝 Contributing

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

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Kaggle for providing high-quality datasets
• Apache Spark community for excellent documentation
• AWS for reliable cloud infrastructure
• Scikit-learn contributors for robust ML tools

---

🚦 Quick Start Commands

# Clone and setup
git clone <repository-url>
cd data-science-demo
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

# Start all services
docker-compose up -d

# Run complete pipeline
python src/main.py --pipeline full

# Run specific stages
python src/main.py --stages data_ingestion preprocessing model_training

# Launch Jupyter Lab
jupyter lab notebooks/

# Access services
# Jupyter Lab: http://localhost:8888
# Spark UI: http://localhost:4040
# MLflow: http://localhost:5000
# Grafana: http://localhost:3000 (admin/admin123)

🔍 Repository Structure Details

data-science-demo/
├── src/                          # Source code modules
│   ├── data_processing/         # PySpark data processing components
│   │   ├── spark_processor.py   # Main distributed processing class
│   │   ├── feature_engineer.py  # Advanced feature engineering
│   │   └── data_validator.py    # Comprehensive data quality checks
│   ├── ml_models/               # Machine learning models
│   │   ├── base_model.py        # Abstract base class for all models
│   │   ├── logistic_model.py    # Logistic regression implementation
│   │   └── random_forest.py     # Random forest implementation
│   └── main.py                  # Pipeline orchestration script
├── notebooks/                   # Jupyter notebooks for analysis
│   ├── 01_data_exploration.ipynb # Comprehensive EDA with visualizations
│   ├── 02_data_processing.ipynb  # PySpark processing examples
│   ├── 03_feature_engineering.ipynb # Feature creation techniques
│   ├── 04_ml_modeling.ipynb     # Model training and evaluation
│   └── 05_business_insights.ipynb # Business analysis and reporting
├── sql/                         # Redshift/PostgreSQL schemas and queries
│   ├── create_tables.sql        # Complete data warehouse schema
│   ├── data_warehouse_etl.sql   # ETL procedures and functions
│   └── analytical_queries.sql   # Business intelligence queries
├── config/                      # Configuration management
│   ├── spark_config.py          # Comprehensive Spark configurations
│   ├── redshift_config.py       # Database connection management
│   └── model_config.yaml        # ML pipeline configuration
├── tests/                       # Unit and integration tests
├── docker-compose.yml           # Multi-service Docker environment
├── Dockerfile                   # Multi-stage container build
└── requirements.txt             # Complete dependency list

📚 Learning Outcomes

This demo demonstrates:

• Data Engineering: Large-scale data processing with PySpark
• Feature Engineering: Advanced techniques for ML feature creation
• Data Warehousing: Dimensional modeling and query optimization
• Machine Learning: End-to-end ML pipeline with proper evaluation
• DevOps: Containerization, orchestration, and monitoring
• Data Quality: Comprehensive validation and quality scoring
• Business Analytics: Translating data insights to business value

🤝 Contributing & Customization

This repository serves as a comprehensive template for data science projects. You can:

• Replace synthetic data with your own datasets
• Add new machine learning models by extending the base model class
• Customize feature engineering for your domain
• Integrate with your preferred cloud providers (AWS, GCP, Azure)
• Extend monitoring and alerting capabilities
• Add more sophisticated data quality rules

This demo showcases enterprise-level data engineering and machine learning practices suitable for production environments.