Simple ETL Hands-On Lab Guide

Online Retail Data Analytics Pipeline

🎯 Learning Objectives

• Build a complete ETL pipeline using Python and Pandas
• Apply 9 different business transformation rules
• Implement data quality checks and validations
• Create analytics-ready datasets for business insights
• Load processed data into a database for reporting

📊 Business Context

Company: Online Retail Store

Dataset: Transaction data including customer purchases, product details, and order information

Challenge: Raw transaction data needs to be processed into meaningful business insights

Business Questions to Answer:

1. What is the total revenue for each transaction?
2. What are the sales trends by month and year?
3. How many distinct products does each customer purchase?
4. Which products are selling the most in terms of quantity?
5. Who are our high-value customers?
6. How can we categorize customers by purchase frequency?