This repository showcases a comprehensive SQL-based analysis of retail bike sales data. It covers schema creation, data loading, and a series of analytical queries to derive actionable insights from transactional records.
This SQL project includes:
- Building a normalized schema with related tables for products, staff, customers, inventory, and orders.
- Loading data from CSVs into a relational database.
- Querying and transforming data using subqueries, CTEs, joins, and aggregation.
- Extracting insights about sales performance, top customers, inventory management, and shipping gaps.
- Which stores and staff generate the most sales
- The top 5 customers based on total revenue generated
- Which brands and product categories perform best
- Identification of low-stock products to manage inventory risks
- Breakdown of unfulfilled orders
- Monthly revenue trends and brand-level sales growth
- SQL (standard syntax with PostgreSQL flavor)
- CSV files as flat data sources
- SQL Server