relational-sql-bike-analytics

A structured, purely relational SQL analytics project optimizing multi-table JOINs and aggregate functions to evaluate retail performance, customer distributions, and zero-stock inventory metrics for a bicycle retail chain.

🗄️ Database Schema & File Structure

The project utilizes 9 structured CSV datasets representing a typical retail and production ecosystem:

• [cite_start]stores.csv: Contains store details including store_id and store_name[cite: 4, 18].
• [cite_start]customers.csv: Holds customer profile data mapped by customer_id[cite: 6].
• [cite_start]orders.csv: Core transaction ledger connecting order_id, customer_id, store_id, and order_date[cite: 6, 18].
• [cite_start]order_items.csv: Line-item details containing order_id, product_id, quantity, list_price, and discount[cite: 18].
• [cite_start]products.csv: Master catalog mapping product_id, product_name, brand_id, and category_id[cite: 5, 7].
• brands.csv: Contains manufacturer names mapped by brand_id[cite: 7].
• categories.csv: Structural categorization for inventory classes.
• stocks.csv: Real-time warehouse balance tracking matching store_id, product_id, and quantity[cite: 8].
• staffs.csv: Employee operational assignments across branches.

🔗 Data Source

The relational tables utilized in this analysis were extracted from the Bike Store Sample Database on Kaggle (originally sourced from standard relational database sample collections).