This project contains a complete SQL analysis of a Music Store database. It includes beginner to advanced SQL queries designed to extract meaningful business insights from customer data, sales performance, artist popularity, and music genre trends.
🎵 Music Store SQL Data Analysis Project
This repository contains a complete SQL-based data analysis project using the classic Music Store Database. The project explores real business questions related to customer behavior, revenue trends, popular artists, and genre performance. It includes beginner, intermediate, and advanced SQL queries to help you understand data analytics using SQL in a structured and practical way.
📖 Project Overview The Music Store Database simulates a real-world digital music shop. It has customer records, employee details, invoice-level purchase data, songs (tracks), playlists, artists, albums, and genres. This project uses SQL to answer important business problems such as: Who are the top customers? Which country generates the highest revenue? Which genre is most popular among customers? Which artists perform best in terms of sales? What patterns do we observe in customer spending across regions? The goal of this project is to show how SQL can be used to gain insights and support business decision-making. 🗂️ Dataset Details (Tables Included) The database contains the following tables: Table Name Description Customer Customer personal and location details Employee Store staff and reporting structure Invoice Complete order-level purchase records Invoice_Line Item-level details of each invoice Track Song list with metadata Genre Music genres (Rock, Pop, Jazz, etc.) Album Album details for each track Artist Artist information Playlist User playlists Playlist_Track Mapping of tracks added to playlists Media_Type Formats of tracks (MPEG, AAC, etc.)
🎯 Project Objectives ✔ Understand the structure of a relational database ✔ Perform joins across multiple tables ✔ Use Aggregate Functions to generate insights ✔ Apply Window Functions for ranking and partitioning ✔ Use CTEs (Common Table Expressions) for complex queries ✔ Solve real-world analytical questions
📊 Key Business Insights Generated 🏆 1. Senior-most Employee Identified the employee with the most years of service using ranking functions.
🌍 2. Country with Highest Invoices Ranked countries based on total invoice counts and revenue contribution.
🏙️ 3. Top Revenue-Generating City Discovered the city with the highest total invoice amount for planning promotional events.
👑 4. Best Customer Found the customer who spent the most money in the store.
🎸 5. Rock Music Listeners Extracted customers who purchased Rock tracks using multi-table joins.
🎤 6. Most Popular Rock Artists Ranked artists based on the number of rock songs sold.
⏱️ 7. Songs Longer Than Average Duration Filtered tracks above average length.
🌎 8. Popular Genre by Country Used window functions to find the most purchased genre per country.
💳 9. Top-Spending Customer Per Country Identified the customer who spent the most in each region.
🧠 Skills Demonstrated This project demonstrates strong command of SQL including:
🔹 SQL Concepts SELECT, WHERE, ORDER BY GROUP BY & Aggregation DISTINCT JOINS (INNER, LEFT, RIGHT) Subqueries Common Table Expressions (CTEs) Window Functions (DENSE_RANK, RANK, OVER())
🔹 Data Analysis Skills Revenue analysis Customer segmentation Artist and genre ranking Country-level insights Trend identification
🔹 Business Intelligence Understanding business metrics Converting data into actionable insights Structuring analysis in a clean, readable format
🧩 Project Structure 📁 Music-Store-SQL-Project │ ├── 📄 music_store SQL PROJECT.sql # Main SQL file with all queries ├── 📘 README.md # Documentation └── 📁 Assets/ # (Optional) Screenshots, diagrams
🚀 How to Run This Project Download or clone the repository: git clone https://github.com/your-username/Music-Store-SQL-Project.git Open your SQL environment (SQL Server, MySQL, PostgreSQL depending on DB format). Load the Music Store Database. Open the SQL file from this repository. cute the queries section-by-section to explore insights.
📈 Future Improvements You can expand this project by adding: Data visualization dashboards (Power BI / Tableau) Stored procedures for common analysis Optimization of long SQL queries ER Diagram for database relationships Python integration for automated analytics
🙋 About the Author Suraj Sahu Aspiring Data Analyst | SQL | Python | Power BI | Data Visualization