Build a Coffee Shop Sales Monitor with Python and SQL

In this project, we’ll build a terminal-based sales analytics pipeline for a coffee shop using Python and SQLite. We’ll first design a relational schema with four related tables, populate it with sample transactional data, and then write SQL queries to extract key business metrics. We’ll use core SQL features such as JOIN, GROUP BY, SUM, COUNT, and ORDER BY. We’ll also use Python’s built-in sqlite3 module to connect to the database and run queries without external dependencies.

By the end of the project, we’ll have a terminal-based sales analytics tool that answers four business questions: which items sell the most, which menu category generates the highest revenue, which hours are busiest, and which staff member processes the most transactions. All four analyses are combined into a formatted report that prints to the terminal with a single command. This creates a clean, repeatable pipeline that can be run on demand.