🎵 SQL Music Store Analysis (Chinook Database Project)

This project contains a full SQL analysis of the Chinook Music Store Database.
The database includes information about customers, invoices, tracks, albums, artists, and genres.
I used SQL to explore the data and extract useful insights such as top customers, most popular music genres, highest revenue cities, and more.

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📁 Project Structure

SQL-Music-Store-Analysis/

│── README.md
│── chinook_erd.png
├── complete_project_queries.sql
└── summary.md

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🎯 Project Goals

• Understand the data stored in the Chinook database
• Analyze customer spending and buying behavior
• Identify the top-performing genres and artists
• Find countries and cities generating the highest revenue
• Use SQL concepts like JOINs, GROUP BY, CTEs, and Window Functions
• Create a clean and simple SQL mini-project

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🗃️ Database Overview

The Chinook database includes these main tables:

• Customer – customer contact details
• Invoice – purchase summaries
• Invoice Line – track-level purchase details
• Track – music track information
• Album – album details
• Artist – artist information
• Genre – music categories
• Employee – support representatives

The complete database diagram is available here:

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🧠 SQL Concepts Used

• Basic SQL (SELECT, WHERE, ORDER BY)
• Joins (INNER JOIN, LEFT JOIN)
• Aggregations (SUM, COUNT, MAX, MIN)
• GROUP BY and HAVING
• Subqueries
• Common Table Expressions (CTE)
• Window Functions (DENSE_RANK, RANK)

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📜 SQL Queries Included

All important SQL queries are stored in: complete_project_queries.sql