This project analyzes a digital music store database using PostgreSQL.
The database contains information about employees, customers, invoices, tracks, playlists, artists, albums, and more.
Through SQL queries, we explore sales trends, customer behavior, popular genres, top artists, and revenue insights.
- PostgreSQL
- SQL (queries, joins, aggregations, subqueries)
- pgAdmin / psql (for running queries)
- Git & GitHub for version control
├── Create_table_query.sql # All CREATE TABLE Query
├── Questions.txt # List of analysis questions
├── Answers.sql # All SQL analysis queries
├── music_store_csv.zip # Contains all 11 dataset files in CSV format
├──📂images/
│ ├── schema_diagram.png # Schema diagram
│ └── output.png # Query Output example
└── README.md # Project documentation
The database has 11 tables:
- Employee → Employee details
- Customer → Customer info
- Invoice → Purchase invoices with billing details and total
- Invoice_Line → Details of tracks purchased in each invoice
- Track → Tracks information
- Playlist → Music playlists
- Playlist_Track → Mapping between playlists and tracks
- Artist → Artist information
- Album → Albums created by artists
- Media_Type → File/media type of tracks
- Genre → Music genres
📊 ER Diagram:
Problem: We want to find out the most popular music Genre for each country. We determine the most popular genre as the genre with the highest amount of purchases. Write a query that returns each country along with the top Genre. For countries where the maximum number of purchases is shared return all Genres
WITH popular_genre AS (
SELECT c.country,g.name AS genre,g.genre_id,
COUNT(il.quantity) AS purchases,
ROW_NUMBER() OVER(PARTITION BY c.country ORDER BY COUNT(il.quantity) DESC) AS RowNo
FROM customer c
JOIN invoice i ON i.customer_id=c.customer_id
JOIN invoice_line il ON il.invoice_id=i.invoice_id
JOIN track t ON t.track_id=il.track_id
JOIN genre g ON g.genre_id=t.genre_id
GROUP BY 1,2,3
ORDER BY 1 ASC ,4 DESC
)
SELECT * FROM popular_genre
WHERE RowNo <=1;- Clone this repository:
git clone https://github.com/Harsh-Belekar/Music-Store-SQL-Analysis.git👤 Harsh Belekar
📍 Data Analyst | Python | SQL | Power BI | Excel | Data Visualization
🔗 LinkedIn | GitHub

