End-to-End Retail Data Warehouse Pipeline

📌 Project Overview

This project simulates a real-world Business Intelligence environment. The goal is to transform raw sales data into a Data Warehouse optimized for business analytics.

I built a complete ETL (Extract, Transform, Load) pipeline using Python and modeled the data into a Star Schema architecture using SQL.

🛠 Tech Stack

• Python: Pandas (Data Cleaning), SQLAlchemy (ORM).
• SQL: SQLite, Window Functions, Joins, Aggregations.
• Data Modeling: Star Schema (Fact Table & Dimension Tables).
• Visualization: Matplotlib.

🏗 Architecture

Data is transformed from a flat file (.csv) into a relational model:

• Fact Table: fact_vendes (Transactions).
• Dimensions: dim_clients (Customers), dim_productes (Products), dim_llocs (Locations).

📊 Business Insights (Examples)

Leveraging advanced SQL queries, the analysis revealed that:

1. The Technology category is the most profitable (17.4% margin).
2. The Furniture category is critically underperforming (only 2.5% margin), suggesting potential issues with logistics costs.

🚀 How to Run

1. Install dependencies: pip install -r requirements.txt
2. Run ETL pipeline: python scripts/etl_pipeline.py
3. Execute analysis: python scripts/executar_sql.py