Retail Database Architecture

Key Achievement: Designed a 7-table relational schema to track Customer Lifetime Value (CLV) and inventory turnover.

PostgreSQL Database Design ETL Pipeline

Technical Summary

I utilized advanced SQL techniques, including primary/foreign key relationships and complex joins to ensure data integrity—applying a clinical-grade precision to retail data sets.

The "What": Technical Methodology

I architected a 3rd Normal Form (3NF) relational database using PostgreSQL. This involved mapping out an Entity Relationship Diagram (ERD) to eliminate data redundancy across seven core tables: Customers, Orders, Products, Categories, Suppliers, Inventory, and Order_Items. By implementing referential integrity constraints and optimized indexing, I created a system capable of handling complex analytical queries, such as calculating month-over-month growth and individual Customer Lifetime Value via SQL window functions.

The "Why": Data Science Impact

Data Science is only as effective as the infrastructure that supports it. This project proves my ability to provide "end-to-end visibility" into the data lifecycle. By building a robust relational model, I ensure that the data fed into predictive models is accurate, structured, and consistent. In a business context, this architecture provides a single source of truth, enabling executives to make real-time decisions regarding inventory management and high-value customer retention strategies.

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