Data Analysis Mar 2026 SQL - MySQL

SQL Data Exploration Project | Orders Analysis

A hands-on SQL exploration project analysing restaurant orders to uncover menu performance, customer behaviour, and order patterns using MySQL.

Problem

The restaurant had raw order data, but no structured way to understand menu performance, spending patterns, or order volume across time.

Solution

I explored the menu and orders tables with SQL, joined the data, and extracted insights that could support reporting and operational decisions.

How it was built

Explore the menu items table

I used SQL to understand the structure and pricing of the menu, comparing item counts, categories, and Italian dishes.

  • Count total items on the menu
  • Find the least and most expensive dishes
  • Count dishes by cuisine category
  • Compare average price by category

Explore the orders table

I reviewed order volume and ordering patterns to understand how customers interacted with the menu over time.

  • Identify the date range of the dataset
  • Count total orders and items ordered
  • Find orders with the highest item count
  • Spot large orders with more than 12 items

Join both tables for insights

After the basic exploration, I joined the tables to identify item popularity, category trends, and high-spend orders.

  • Join menu_items and order_details
  • Identify the most and least ordered items
  • Determine the most popular categories
  • Find the top 5 highest-spend orders

Translate results into business value

The final output is not only SQL practice. It is a business-focused analysis that supports reporting and decision-making.

  • Customer spend patterns
  • Product performance
  • Order distribution and frequency
  • Insights useful for menu and revenue decisions

Tools and technologies

MySQL
Database and query engine
SQL Aggregations
COUNT, AVG, SUM, GROUP BY
Table Joins
Combining order and menu tables
Data Filtering
WHERE, HAVING, ORDER BY, LIMIT

What I learned

Business value: this project mirrors real reporting tasks and shows how SQL can support product, revenue, and customer analysis in a structured way.
Tags SQLMySQLData AnalysisData ExplorationAnalytics
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