🎵 MusicDB Insights – SQL Analytics Project 📌 Project Description / Purpose

This project explores and analyzes a Music Store database using SQL to extract meaningful business insights. It covers everything from beginner-level SELECT queries to advanced analytics using JOINs, GROUP BY, CTEs, and Window Functions. The goal is to demonstrate strong data manipulation, aggregation, and analytical querying skills, showcasing the ability to turn raw data into valuable business insights.

🛠 Tech Stack

SQL (MySQL / SQLite) – Data extraction, cleaning, and analysis

MySQL Workbench / DBeaver – Query execution and visualization

Excel / CSV – Data storage and export

Docker MySQL (optional) – For local environment setup

📂 Data Source

Dataset: Music Store Database (Relational SQL Dataset)

Tables Included:

customers invoices invoice_items tracks albums artists genres employees

Folder Structure:

data/ → CSV files of raw data

sql/ → SQL query files (Beginner to Advanced)

docs/ → Query results, screenshots, and reports

✨ Features / Highlights 1️⃣ Business Problem

The music store wanted to understand customer preferences, top-performing artists, and revenue trends to improve marketing strategies and optimize inventory.

2️⃣ Goal of the Project

Query and explore customer, artist, and sales data using SQL.

Identify top customers, best-selling genres, and most popular artists.

Demonstrate the ability to use CTEs, subqueries, and ranking functions to derive deep business insights.

3️⃣ Walkthrough of Key Analysis

Beginner Queries: SELECT, WHERE, ORDER BY, and filtering conditions.

Intermediate Queries: Data aggregation using GROUP BY, HAVING, and multi-table JOINs.

Advanced Queries:

*CTEs for reusable query logic.

*RANK() & DENSE_RANK() for ranking artists and customers.

*Nested queries to extract top performers.

*Window Functions for trend and segmentation analysis.

4️⃣ Business Insights & Outcomes

🎧 Top Artists: Identified artists generating the highest global sales revenue.

💽 Popular Genres: Found which music genres perform best by region.

👥 Top Customers: Ranked customers based on total purchase value and frequency.

🌍 Regional Insights: Determined countries with the highest total revenue and average customer spend.

📈 Insights Summary Metric Description Example Insight Top Genre Most purchased music category Rock Top Artist Highest revenue-generating artist AC/DC Top Customer Highest spending customer Helena Holý (Czech Republic) Best Market Country with the most total sales USA

🧠 Key Learnings

Mastered data aggregation, joins, subqueries, and window functions for analytical reporting.

Practiced converting raw data into business-driven insights.

Strengthened understanding of SQL-based data modeling and querying logic.

Built an end-to-end analytical workflow — from data ingestion to insight presentation.

📂 Folder Structure MusicDB-Insights/ ├─ README.md ├─ data/ │ ├─ customers.csv │ ├─ invoices.csv │ ├─ tracks.csv │ └─ ... ├─ sql/ │ ├─ beginner_queries.sql │ ├─ intermediate_queries.sql │ ├─ advanced_queries.sql │ └─ analysis_queries.sql ├─ docs/ │ ├─ query_results.docx │ ├─ screenshots/ │ └─ notes.pdf └─ assets/ ├─ schema_diagram.png └─ query_result.png

👨‍💻 Author

Durga Prasnath Yesu 📍 Aspiring Data Analyst | SQL | Excel | Power BI 🔗 https://www.linkedin.com/in/durga-prasanth-yesu-824945230/ • https://github.com/prasanth5566