🍕 Pizza Sales SQL Project – Complete Project Description

This project is a comprehensive SQL-based analysis of pizza sales data for a restaurant business. It focuses on creating a structured database, loading operational datasets, and running analytical SQL queries to extract meaningful insights about orders, revenue, customer trends, and product performance.

• Using SQL, the project demonstrates how a data analyst can transform raw transactional data into actionable insights that support decision-making in the food service industry.

🎯 Project Objectives :

• Design a relational database for pizza sales
• Import and organize order data, pizza details, categories, and sizes
• Analyze total revenue, sales trends, and order volume
• Identify best-selling and worst-selling pizzas
• Evaluate category-wise and size-wise performance
• Generate KPIs using SQL queries
• Demonstrate SQL skills (joins, aggregations, CTEs, window functions, subqueries)

🧰 Tech Stack : Database

• MySQL (used in the SQL file)

Languages

• SQL (DDL + DML + analytical queries)

Tools

• MySQL Workbench SQL Server / PostgreSQL (optional)

🗄️ Database Schema : The SQL file creates and uses a database named pizzahut, containing the following tables:

1. orders

• Stores high-level order information including: (a) order_id (b) order_date (c) order_time

2. order_details

• Contains item-level information: (a) order_details_id (b) order_id (FK) (c) pizza_id (d) quantity

3. pizzas

• Includes all pizza items the restaurant offers: (a) pizza_id (b) pizza_type_id (c) size (d) price

4. pizza_types

• Describes pizza varieties: (a) pizza_type_id (b) name (c) category (d) ingredients

📊 Key Analyses Performed

The SQL file contains more than 40 business-focused queries, including:

• ✔ Total revenue generated

SELECT SUM(order_details.quantity * pizzas.price) AS total_sales

• ✔ Top-selling and least-selling pizzas

• ✔ Category-wise revenue and order distribution

• ✔ Pizza size performance analysis

• ✔ Daily, weekly & monthly sales trends

• ✔ Peak ordering hours

• ✔ Revenue contribution by pizza type

• ✔ Average Order Value (AOV)

• ✔ CTEs and window function analyses

• ✔ Ranking pizzas by revenue

• ✔ Running totals

• ✔ Percentage contributions

• ✔ This collection of queries helps provide insights that a restaurant might use for inventory planning, menu adjustments, marketing, and staffing.

👨‍💻 Developed By -- Ayush SQL | Data Analysis | BI | Python

• 🔗 GitHub: https://github.com/ayush13-0
• 🔗 LinkedIn: https://www.linkedin.com/in/ayush130