Sales Analytics & Query Optimization Project

### Retail Sales Analytics System

A comprehensive data engineering project that simulates a real-world retail sales environment using SQL Server. The project focuses on designing a scalable relational database, building analytical queries, and generating meaningful business insights.

The system was built using a normalized schema consisting of Customers, Products, Orders, and Order Items tables, ensuring strong data integrity through primary and foreign key relationships.

From a data engineering perspective, the project emphasizes transforming raw transactional data into actionable insights. Advanced SQL techniques such as CTEs, window functions, and optimized queries were used to support key analytics use cases including revenue calculation with discounts, monthly performance tracking, customer activity analysis, and top-selling product identification.

To enhance performance, indexing strategies were applied on critical columns like order_date, customer_id, and composite fields, significantly improving query execution time and scalability.

Additionally, analytical SQL views were created to serve as reusable data layers, enabling efficient reporting on monthly revenue, active customers, and product performance.

Key business insights were derived from the data, including seasonal sales trends, regional performance variations, and customer segmentation based on spending behavior.

This project demonstrates strong capabilities in data modeling, query optimization, and building data-driven systems aligned with real-world business needs.

**Tech Stack:** SQL Server, T-SQL, CTEs, Window Functions, Indexing