E-commerce Data Engineering Pipeline - Hands-On Course

Welcome to the comprehensive data engineering course! This project will take you through building a complete, production-ready data pipeline from scratch using modern tools and cloud services.

Course Overview

This hands-on course teaches you to build an end-to-end data engineering pipeline that processes e-commerce data using industry-standard tools. You'll go from raw data to actionable insights while learning best practices used by data engineering teams worldwide.

What You'll Build:

1. Data Pipeline: Complete ETL process with synthetic e-commerce data
2. Cloud Integration: AWS S3 data lake for scalable storage
3. Workflow Orchestration: Professional orchestration with Prefect
4. Containerization: Production-ready Docker deployment
5. Data Processing: Advanced transformations and business metrics

Architecture Diagram

📊 Sample Data    →    📤 S3 Upload    →    🧹 Processing    →    📈 Metrics    →    ☁️ S3 Storage
    (CSV Files)         (Raw Data)         (Transform)        (Analytics)       (Processed)
        ↓                    ↓                  ↓                  ↓                ↓
   Python Scripts    →   boto3/S3 API    →   Pandas/ETL     →   Aggregations  →  Data Lake
                                              ↓
                                         🔄 Prefect Flows (Orchestration)
                                              ↓
                                         🐳 Docker Containers (Production)

🛠️ Technology Stack

Category	Technology	Purpose
Language	Python 3.12	Core development language
Cloud Platform	AWS S3	Scalable data lake storage
Orchestration	Prefect 3.x	Workflow management & monitoring
Data Processing	Pandas, Polars	Data transformation & analysis
Containerization	Docker + Docker Compose	Production deployment
Data Validation	Great Expectations	Data quality assurance
Package Management	pip, requirements.txt	Dependency management

Project Structure

Hands-On-Project/
├── 📄 .env                              # AWS credentials (create this!)
├── 📊 data/                             # Sample datasets
│   ├── raw/                             # Raw CSV files
│   ├── customers.csv                    # Customer data
│   ├── products.csv                     # Product catalog
│   ├── orders.csv                       # Order transactions
│   ├── order_items.csv                  # Order line items
│   └── reviews.csv                      # Customer reviews
├── 🔧 data-pipeline/                    # Main pipeline code
│   ├── 📄 requirements.txt              # Python dependencies
│   ├── 🐳 docker-compose.yml            # Container orchestration
│   ├── 📂 src/                          # Source code
│   │   ├── data_ingestion/              # S3 upload scripts
│   │   │   └── s3_uploader.py           # Upload data to AWS S3
│   │   ├── data_processing/             # ETL transformations
│   │   │   └── etl_processor.py         # Clean & transform data
│   │   └── orchestration/               # Workflow management
│   │       └── prefect_flows.py         # Prefect orchestration
│   ├── 📂 config/                       # Configuration files
│   │   ├── aws_config.yaml              # AWS settings
│   │   ├── prefect_config.yaml          # Prefect configuration
│   │   └── data_schemas.yaml            # Data validation rules
│   ├── 📂 infrastructure/               # Container infrastructure
│   │   └── docker/
│   │       └── Dockerfile               # Container build instructions
│   └── 📂 tests/                        # Test files
│       └── test_aws_connection.py       # AWS connectivity tests
└── 📚 CONTAINERIZATION_GUIDE.md         # Complete Docker guide

Quick Start Guide

Prerequisites Checklist

• Python 3.10+ installed (recommended: 3.12)
• AWS Account with S3 access
• Docker Desktop installed and running
• Git for version control
• Text Editor (VS Code recommended)

Step 1: Environment Setup

1. Clone/Download the project

   cd "D:\Hands-On-Project"

2. Create virtual environment

   python -m venv venv
   venv\Scripts\activate  # Windows

3. Install dependencies

   cd data-pipeline
   pip install -r requirements.txt

4. Configure AWS credentials Create .env file in project root:

   AWS_ACCESS_KEY_ID=your_access_key_here
   AWS_SECRET_ACCESS_KEY=your_secret_key_here
   AWS_DEFAULT_REGION=us-east-1
   AWS_S3_BUCKET_NAME=your-unique-bucket-name

🔧 Step 2: Test Your Setup

1. Test AWS connection

   python tests/test_aws_connection.py

   ✅ Should show: "Connection successful!"

2. Extract sample data

   # Data should already be extracted in data/raw/ folder
   ls ../data/raw/  # Should show 5 CSV files

📤 Step 3: Run Data Pipeline (Local)

1. Upload raw data to S3

   python src/data_ingestion/s3_uploader.py

   ✅ Should upload 5 CSV files to S3

2. Process data

   python src/data_processing/etl_processor.py

   ✅ Should clean data and create business metrics

3. Run orchestrated pipeline

   python src/orchestration/prefect_flows.py

   ✅ Should show complete pipeline execution

🔄 Step 4: Prefect Orchestration

1. Start Prefect server

   prefect server start

2. Access Prefect UI Open: http://localhost:4200 ✅ Should show Prefect dashboard

3. View pipeline execution

   • See flow runs, tasks, and logs
   • Monitor performance and errors

🐳 Step 5: Containerization (Production)

1. Build and start containers

   cd "D:\Hands-On-Project"
   docker-compose -f data-pipeline/docker-compose.yml up --build -d

2. Run containerized pipeline

   docker exec -it data-pipeline-data-pipeline-1 python data-pipeline/src/orchestration/prefect_flows.py

3. Access containerized Prefect UI Open: http://localhost:4200 ✅ Should show pipeline execution in containers

📊 What You'll Learn

Module 1: Data Pipeline Fundamentals

• ✅ ETL vs ELT patterns
• ✅ Data lake architecture
• ✅ Batch processing concepts
• ✅ Error handling strategies

Module 2: Cloud Data Engineering

• ✅ AWS S3 data lake setup
• ✅ IAM permissions and security
• ✅ Data partitioning strategies
• ✅ Cost optimization techniques

Module 3: Workflow Orchestration

• ✅ Prefect flow design
• ✅ Task dependencies
• ✅ Scheduling and triggers
• ✅ Monitoring and alerting

Module 4: Production Deployment

• ✅ Docker containerization
• ✅ Multi-service architecture
• ✅ Environment management
• ✅ Scaling strategies

Module 5: Data Quality & Testing

• ✅ Data validation patterns
• ✅ Unit testing for data pipelines
• ✅ Great Expectations framework
• ✅ Pipeline monitoring

Sample Data Overview

Your pipeline processes realistic e-commerce data:

Dataset	Records	Description
customers.csv	1,000+	Customer profiles, demographics
products.csv	500+	Product catalog, categories, pricing
orders.csv	2,000+	Order transactions, timestamps
order_items.csv	6,000+	Individual items per order
reviews.csv	1,500+	Customer reviews and ratings

🔧 Troubleshooting Guide

Common Issues:

❌ "AWS credentials not found"

• Solution: Create .env file with valid AWS credentials
• Check: File is in project root, not inside data-pipeline folder

❌ "Docker daemon not running"

• Solution: Start Docker Desktop application
• Check: Docker icon in system tray shows running status

❌ "Can't connect to Prefect server"

• Solution: Wait 30 seconds after starting containers
• Check: http://localhost:4200 loads properly

❌ "Python module not found"

• Solution: Activate virtual environment and reinstall requirements
• Check: pip list shows all required packages

Project Completion

By the end of this course, you'll have built:

✅ Production-ready data pipeline processing real e-commerce data
✅ Cloud-based data lake on AWS S3
✅ Professional orchestration with Prefect monitoring
✅ Containerized deployment ready for any environment
✅ Comprehensive understanding of modern data engineering

Next Steps & Career Path

After completing this project:

1. Add to your portfolio - Showcase on GitHub and LinkedIn
2. Extend functionality - Add real-time streaming, machine learning
3. Learn advanced tools - Apache Airflow, dbt, Snowflake
4. Apply for roles - Data Engineer, Analytics Engineer, Platform Engineer

Support & Resources

• Full Documentation: See CONTAINERIZATION_GUIDE.md
• Issues: Check troubleshooting section above
• Questions: Review code comments and documentation
• Best Practices: Follow the patterns in provided code

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🎯 Ready to become a data engineer? Let's build something amazing! 🚀