Exploring sales and customer data from the Chinook music store to answer real business questions using SQL.
The Chinook database contains simulated data for an online music store, with tables for customers, invoices, tracks, artists, and genres. Source: https://github.com/lerocha/chinook-database
Use SQL to explore purchasing patterns and identify which customers, genres, and regions contribute most to revenue.
This project showcases SQL proficiency through six analysis queries using joins, filtering, aggregation, and sorting.
Skills demonstrated:
- Data exploration and filtering (
SELECT,WHERE,ORDER BY) - Aggregation and grouping (
SUM,AVG,GROUP BY) - Table joins (
INNER JOIN) - Deriving business insights from transactional data
Goal: Identify the highest-value invoices from U.S. customers.
Insight: U.S. customers generated several large invoices, particularly from California and New York, indicating strong sales concentration in those states.
Goal: Determine which countries contribute the most total revenue.
Insight: The United States leads in revenue, followed by Canada, Brazil, and France — key regions for customer engagement.
Goal: Discover which artists generate the most revenue from track sales.
Insight: Rock artists dominate, with AC/DC, Queen, and Metallica leading sales — reflecting the catalog’s genre bias.
Goal: Compare average order size across markets.
Insight: The U.S. and Canada have the highest average invoice totals, suggesting higher purchasing power per customer.
Goal: See which employees manage the most customer revenue.
Insight: Each representative manages roughly equal revenue, but Jane Peacock leads slightly, indicating strong customer retention.
Goal: Identify which genres drive the most sales.
Insight: Rock, Latin, and Metal are top-performing genres, accounting for nearly half of total revenue.
| File | Description |
|---|---|
chinook.sql |
Final SQL queries |
results/ |
CSV exports or screenshots (optional) |
README.md |
Documentation and insights |
- Download Chinook_Sqlite.sqlite.
- Open it in VS Code with the SQLite extension or in DB Browser for SQLite.
- Run the queries in
chinook.sql. - Compare outputs with the insights above.
- Demonstrated ability to query, aggregate, and interpret real data.
- Practiced translating raw output into meaningful insights.
- This project forms the foundation for advanced analytics with Python & visualization tools.
- SQLite (database)
- VS Code (query execution)
- Rainbow CSV (CSV inspection)
- Git & GitHub (version control)
Stephen Schier
LinkedIn • GitHub
This project is licensed under the MIT License.