E-commerce Data Pipeline & Analytics Platform

Project Overview

This project demonstrates a complete end-to-end data engineering solution for an e-commerce platform, showcasing ETL pipelines, data warehousing, and analytics capabilities suitable for an Analytics Engineer role.

Business Context

An e-commerce company needs to:

• Track customer orders, products, and user behavior
• Build a dimensional data warehouse for analytics
• Monitor data quality and pipeline health
• Generate daily business reports and insights
• Scale data processing for growing business needs

Technical Architecture

Data Sources (Simulated)

• Transactional Database (PostgreSQL): Orders, Products, Customers, Payments
• Event Logs (CSV/JSON): User clickstream, app events
• External APIs: Payment gateway, shipping providers

Data Warehouse (PostgreSQL)

• Staging Layer: Raw data ingestion
• Core Layer: Cleaned and transformed data
• Dimensional Model: Star schema with fact and dimension tables
  • Fact: fact_orders, fact_daily_sales
  • Dimensions: dim_customers, dim_products, dim_date, dim_locations

Tech Stack

• Language: Python
• Database: PostgreSQL
• Orchestration: Apache Airflow
• Data Processing: Pandas, SQLAlchemy
• Data Quality: Custom validation framework
• Visualization: SQL queries for BI tools

Project Structure

analytics_engineer_project/
├── README.md
├── requirements.txt
├── config/
│   ├── database_config.yaml
│   └── pipeline_config.yaml
├── data/
│   ├── sample_data/
│   └── data_generator.py
├── sql/
│   ├── schema/
│   │   ├── 01_create_staging_tables.sql
│   │   ├── 02_create_core_tables.sql
│   │   └── 03_create_dimensional_model.sql
│   ├── transforms/
│   │   ├── staging_to_core.sql
│   │   └── core_to_dimensional.sql
│   └── analytics/
│       ├── daily_sales_report.sql
│       └── customer_metrics.sql
├── src/
│   ├── etl/
│   │   ├── __init__.py
│   │   ├── extract.py
│   │   ├── transform.py
│   │   └── load.py
│   ├── data_quality/
│   │   ├── __init__.py
│   │   ├── validators.py
│   │   └── monitors.py
│   ├── utils/
│   │   ├── __init__.py
│   │   ├── db_connector.py
│   │   └── logger.py
│   └── orchestration/
│       ├── dags/
│       │   ├── daily_etl_pipeline.py
│       │   ├── data_quality_checks.py
│       │   └── dimensional_model_refresh.py
│       └── tasks/
├── tests/
│   ├── test_etl.py
│   └── test_data_quality.py
├── notebooks/
│   └── exploratory_analysis.ipynb
└── docs/
    ├── architecture.md
    ├── data_dictionary.md
    └── setup_guide.md

Key Features

1. ETL Pipeline

• Extract: Pull data from multiple sources (databases, files, APIs)
• Transform: Data cleaning, validation, business logic application
• Load: Incremental and full load strategies into data warehouse

2. Data Warehouse Design

• Implemented star schema for optimal query performance
• SCD Type 2 for tracking historical changes
• Partitioning strategies for large fact tables
• Indexing for query optimization

3. Workflow Orchestration (Airflow)

• Scheduled DAGs for daily, hourly pipelines
• Dependency management between tasks
• Error handling and retry logic
• Email/Slack notifications on failures

4. Data Quality Framework

• Schema validation
• Data completeness checks
• Business rule validation
• Anomaly detection
• Data profiling and statistics

5. Monitoring & Logging

• Pipeline execution metrics
• Data quality dashboards
• Error tracking and alerting
• Performance monitoring

Skills Demonstrated

✅ ETL Development: Complete extract, transform, load processes
✅ Python Proficiency: Object-oriented design, pandas, SQLAlchemy
✅ SQL Mastery: Complex queries, window functions, CTEs, optimization
✅ Data Warehousing: Dimensional modeling, star schema, SCD
✅ Workflow Orchestration: Apache Airflow DAGs, task dependencies
✅ Data Quality: Validation frameworks, monitoring, alerting
✅ Database Management: PostgreSQL, connection pooling, transactions
✅ Best Practices: Code organization, documentation, testing

Quick Start

Prerequisites

- Python 3.9+
- PostgreSQL 13+
- Apache Airflow 2.0+

Installation

# Clone the repository
cd analytics_engineer_project

# Create Python virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Set up PostgreSQL data warehouse database
createdb ecommerce_dwh

# Run database schema creation
psql -d ecommerce_dwh -f sql/schema/01_create_staging_tables.sql
psql -d ecommerce_dwh -f sql/schema/02_create_core_tables.sql
psql -d ecommerce_dwh -f sql/schema/03_create_dimensional_model.sql

# Update database password in config/database_config.yaml
# Change 'your_password_here' to your actual postgres password

# Generate sample data (loads directly into staging tables)
python data/data_generator.py

# Optional: Initialize Airflow (for workflow orchestration)
export AIRFLOW_HOME=$(pwd)/airflow
airflow db init
airflow users create --username admin --password admin --firstname Admin --lastname User --role Admin --email admin@example.com

# Start Airflow (optional)
airflow webserver -p 8080 &
airflow scheduler &

Running the Pipeline

# Run ETL manually (after generating sample data)
python src/etl/transform.py  # Transform staging → core
python src/etl/load.py        # Load core → analytics

# Run data quality checks
python src/data_quality/validators.py

# Run pipeline monitoring
python src/data_quality/monitors.py

# Or trigger complete Airflow DAG (if Airflow is set up)
airflow dags trigger daily_etl_pipeline

Business Metrics & Reports

The pipeline generates key business metrics:

• Daily/Monthly GMV (Gross Merchandise Value)
• Customer Acquisition & Retention metrics
• Product Performance analysis
• Order Fulfillment metrics
• Payment Success rates
• Customer Lifetime Value (CLV)

Future Enhancements

• Integration with Apache Spark for big data processing
• Real-time streaming with Kafka
• dbt for transformation layer
• CI/CD pipeline with GitHub Actions
• Docker containerization
• Kubernetes deployment
• Integration with BI tools (Tableau, Power BI, Metabase)