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Chinook SQL Analysis

Exploring sales and customer data from the Chinook music store to answer real business questions using SQL.

Dataset

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

Objective

Use SQL to explore purchasing patterns and identify which customers, genres, and regions contribute most to revenue.

Overview

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

Business Questions Answered

1. Top 10 invoices from U.S. customers

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.

2. Revenue by country

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.

3. Top-selling artists by revenue

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.

4. Average invoice value by country

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.

5. Support representative revenue performance

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.

6. Revenue by genre

Goal: Identify which genres drive the most sales.
Insight: Rock, Latin, and Metal are top-performing genres, accounting for nearly half of total revenue.

Folder Contents

FileDescription
chinook.sqlFinal SQL queries
results/CSV exports or screenshots (optional)
README.mdDocumentation and insights

How to Reproduce

  1. Download Chinook_Sqlite.sqlite.
  2. Open it in VS Code with the SQLite extension or in DB Browser for SQLite.
  3. Run the queries in chinook.sql.
  4. Compare outputs with the insights above.

Key Takeaways

  • 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.

Tools Used

  • SQLite (database)
  • VS Code (query execution)
  • Rainbow CSV (CSV inspection)
  • Git & GitHub (version control)

Author

Stephen Schier
LinkedIn • GitHub

Acknowledgments

License

This project is licensed under the MIT License.

About

SQL portfolio project analyzing Chinook music store data

Resources

Readme
MIT license
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

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, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
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(function() {
function addCopyButtons() {
document.querySelectorAll('pre code').forEach(function(codeBlock) {
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var __re = new RegExp('^' + "github\\.com" + '

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Repository files navigation
Chinook SQL Analysis
Exploring sales and customer data from the Chinook music store to answer real business questions using SQL.
Dataset
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
Objective
Use SQL to explore purchasing patterns and identify which customers, genres, and regions contribute most to revenue.
Overview
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
Business Questions Answered
1. Top 10 invoices from U.S. customers
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.
2. Revenue by country
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.
3. Top-selling artists by revenue
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.
4. Average invoice value by country
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.
5. Support representative revenue performance
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.
6. Revenue by genre
Goal: Identify which genres drive the most sales.
Insight: Rock, Latin, and Metal are top-performing genres, accounting for nearly half of total revenue.
Folder Contents
FileDescription
chinook.sqlFinal SQL queries
results/CSV exports or screenshots (optional)
README.mdDocumentation and insights
How to Reproduce
  1. Download Chinook_Sqlite.sqlite.
  2. Open it in VS Code with the SQLite extension or in DB Browser for SQLite.
  3. Run the queries in chinook.sql.
  4. Compare outputs with the insights above.
Key Takeaways
  • 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.
Tools Used
  • SQLite (database)
  • VS Code (query execution)
  • Rainbow CSV (CSV inspection)
  • Git & GitHub (version control)
Author
Stephen Schier
LinkedIn • GitHub
Acknowledgments
License
This project is licensed under the MIT License.
About
SQL portfolio project analyzing Chinook music store data
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
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0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Force GitHub README to respect dark mode
(function() {
var style = document.createElement('style');
style.textContent = '
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color-scheme: dark light;
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
Chinook SQL Analysis
Exploring sales and customer data from the Chinook music store to answer real business questions using SQL.
Dataset
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
Objective
Use SQL to explore purchasing patterns and identify which customers, genres, and regions contribute most to revenue.
Overview
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
Business Questions Answered
1. Top 10 invoices from U.S. customers
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.
2. Revenue by country
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.
3. Top-selling artists by revenue
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.
4. Average invoice value by country
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.
5. Support representative revenue performance
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.
6. Revenue by genre
Goal: Identify which genres drive the most sales.
Insight: Rock, Latin, and Metal are top-performing genres, accounting for nearly half of total revenue.
Folder Contents
FileDescription
chinook.sqlFinal SQL queries
results/CSV exports or screenshots (optional)
README.mdDocumentation and insights
How to Reproduce
  1. Download Chinook_Sqlite.sqlite.
  2. Open it in VS Code with the SQLite extension or in DB Browser for SQLite.
  3. Run the queries in chinook.sql.
  4. Compare outputs with the insights above.
Key Takeaways
  • 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.
Tools Used
  • SQLite (database)
  • VS Code (query execution)
  • Rainbow CSV (CSV inspection)
  • Git & GitHub (version control)
Author
Stephen Schier
LinkedIn • GitHub
Acknowledgments
License
This project is licensed under the MIT License.
About
SQL portfolio project analyzing Chinook music store data
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
(function() {
var ref = document.referrer;
var terms = [];
if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {
var url = new URL(ref);
var q = url.searchParams.get('q') || url.searchParams.get('p');
if (q) {
terms = q.split(/\s+/).filter(function(t) { return t.length > 2; });
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if (terms.length === 0) return;
var style = document.createElement('style');
style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';
document.head.appendChild(style);
function highlight(node) {
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observer.observe(document.body, { childList: true, subtree: true });
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}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
Chinook SQL Analysis
Exploring sales and customer data from the Chinook music store to answer real business questions using SQL.
Dataset
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
Objective
Use SQL to explore purchasing patterns and identify which customers, genres, and regions contribute most to revenue.
Overview
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
Business Questions Answered
1. Top 10 invoices from U.S. customers
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.
2. Revenue by country
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.
3. Top-selling artists by revenue
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.
4. Average invoice value by country
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.
5. Support representative revenue performance
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.
6. Revenue by genre
Goal: Identify which genres drive the most sales.
Insight: Rock, Latin, and Metal are top-performing genres, accounting for nearly half of total revenue.
Folder Contents
FileDescription
chinook.sqlFinal SQL queries
results/CSV exports or screenshots (optional)
README.mdDocumentation and insights
How to Reproduce
  1. Download Chinook_Sqlite.sqlite.
  2. Open it in VS Code with the SQLite extension or in DB Browser for SQLite.
  3. Run the queries in chinook.sql.
  4. Compare outputs with the insights above.
Key Takeaways
  • 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.
Tools Used
  • SQLite (database)
  • VS Code (query execution)
  • Rainbow CSV (CSV inspection)
  • Git & GitHub (version control)
Author
Stephen Schier
LinkedIn • GitHub
Acknowledgments
License
This project is licensed under the MIT License.
About
SQL portfolio project analyzing Chinook music store data
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Strip utm_, fbclid, gclid, etc. from all links on page
(function() {
var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
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});
return changed ? u.toString() : url;
} catch (e) {
return url;
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}
function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
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var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
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var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Repository files navigation
Chinook SQL Analysis
Exploring sales and customer data from the Chinook music store to answer real business questions using SQL.
Dataset
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
Objective
Use SQL to explore purchasing patterns and identify which customers, genres, and regions contribute most to revenue.
Overview
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
Business Questions Answered
1. Top 10 invoices from U.S. customers
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.
2. Revenue by country
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.
3. Top-selling artists by revenue
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.
4. Average invoice value by country
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.
5. Support representative revenue performance
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.
6. Revenue by genre
Goal: Identify which genres drive the most sales.
Insight: Rock, Latin, and Metal are top-performing genres, accounting for nearly half of total revenue.
Folder Contents
FileDescription
chinook.sqlFinal SQL queries
results/CSV exports or screenshots (optional)
README.mdDocumentation and insights
How to Reproduce
  1. Download Chinook_Sqlite.sqlite.
  2. Open it in VS Code with the SQLite extension or in DB Browser for SQLite.
  3. Run the queries in chinook.sql.
  4. Compare outputs with the insights above.
Key Takeaways
  • 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.
Tools Used
  • SQLite (database)
  • VS Code (query execution)
  • Rainbow CSV (CSV inspection)
  • Git & GitHub (version control)
Author
Stephen Schier
LinkedIn • GitHub
Acknowledgments
License
This project is licensed under the MIT License.
About
SQL portfolio project analyzing Chinook music store data
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
btn.click();
}
}
// Try immediately
tryTheater();
// Try after navigation (SPA)
var lastUrl = location.href;
setInterval(function() {
if (location.href !== lastUrl) {
lastUrl = location.href;
setTimeout(tryTheater, 500);
}
}, 1000);
// Also try on player load
var observer = new MutationObserver(tryTheater);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
Chinook SQL Analysis
Exploring sales and customer data from the Chinook music store to answer real business questions using SQL.
Dataset
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
Objective
Use SQL to explore purchasing patterns and identify which customers, genres, and regions contribute most to revenue.
Overview
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
Business Questions Answered
1. Top 10 invoices from U.S. customers
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.
2. Revenue by country
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.
3. Top-selling artists by revenue
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.
4. Average invoice value by country
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.
5. Support representative revenue performance
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.
6. Revenue by genre
Goal: Identify which genres drive the most sales.
Insight: Rock, Latin, and Metal are top-performing genres, accounting for nearly half of total revenue.
Folder Contents
FileDescription
chinook.sqlFinal SQL queries
results/CSV exports or screenshots (optional)
README.mdDocumentation and insights
How to Reproduce
  1. Download Chinook_Sqlite.sqlite.
  2. Open it in VS Code with the SQLite extension or in DB Browser for SQLite.
  3. Run the queries in chinook.sql.
  4. Compare outputs with the insights above.
Key Takeaways
  • 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.
Tools Used
  • SQLite (database)
  • VS Code (query execution)
  • Rainbow CSV (CSV inspection)
  • Git & GitHub (version control)
Author
Stephen Schier
LinkedIn • GitHub
Acknowledgments
License
This project is licensed under the MIT License.
About
SQL portfolio project analyzing Chinook music store data
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {
el.style.position = 'static';
el.style.top = 'auto';
el.style.zIndex = 'auto';
}
});
}
unstick();
var observer = new MutationObserver(unstick);
observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
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Chinook SQL Analysis
Exploring sales and customer data from the Chinook music store to answer real business questions using SQL.
Dataset
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
Objective
Use SQL to explore purchasing patterns and identify which customers, genres, and regions contribute most to revenue.
Overview
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
Business Questions Answered
1. Top 10 invoices from U.S. customers
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.
2. Revenue by country
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.
3. Top-selling artists by revenue
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.
4. Average invoice value by country
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.
5. Support representative revenue performance
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.
6. Revenue by genre
Goal: Identify which genres drive the most sales.
Insight: Rock, Latin, and Metal are top-performing genres, accounting for nearly half of total revenue.
Folder Contents
FileDescription
chinook.sqlFinal SQL queries
results/CSV exports or screenshots (optional)
README.mdDocumentation and insights
How to Reproduce
  1. Download Chinook_Sqlite.sqlite.
  2. Open it in VS Code with the SQLite extension or in DB Browser for SQLite.
  3. Run the queries in chinook.sql.
  4. Compare outputs with the insights above.
Key Takeaways
  • 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.
Tools Used
  • SQLite (database)
  • VS Code (query execution)
  • Rainbow CSV (CSV inspection)
  • Git & GitHub (version control)
Author
Stephen Schier
LinkedIn • GitHub
Acknowledgments
License
This project is licensed under the MIT License.
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SQL portfolio project analyzing Chinook music store data
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Readme
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chinook.sql
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Repository files navigation
Chinook SQL Analysis
Exploring sales and customer data from the Chinook music store to answer real business questions using SQL.
Dataset
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
Objective
Use SQL to explore purchasing patterns and identify which customers, genres, and regions contribute most to revenue.
Overview
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
Business Questions Answered
1. Top 10 invoices from U.S. customers
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.
2. Revenue by country
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.
3. Top-selling artists by revenue
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.
4. Average invoice value by country
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.
5. Support representative revenue performance
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.
6. Revenue by genre
Goal: Identify which genres drive the most sales.
Insight: Rock, Latin, and Metal are top-performing genres, accounting for nearly half of total revenue.
Folder Contents
FileDescription
chinook.sqlFinal SQL queries
results/CSV exports or screenshots (optional)
README.mdDocumentation and insights
How to Reproduce
  1. Download Chinook_Sqlite.sqlite.
  2. Open it in VS Code with the SQLite extension or in DB Browser for SQLite.
  3. Run the queries in chinook.sql.
  4. Compare outputs with the insights above.
Key Takeaways
  • 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.
Tools Used
  • SQLite (database)
  • VS Code (query execution)
  • Rainbow CSV (CSV inspection)
  • Git & GitHub (version control)
Author
Stephen Schier
LinkedIn • GitHub
Acknowledgments
License
This project is licensed under the MIT License.
About
SQL portfolio project analyzing Chinook music store data
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors