Overview
An independent SQL analysis of the Chinook sample music-store database, exploring business questions about sales and purchasing patterns across customers, invoices, tracks, artists, genres, and employees.
Problem Statement
Understanding business dynamics across an e-commerce music store requires querying relational schemas to evaluate purchasing trends, invoice distributions, and employee sales patterns.
Business analysts, store managers, and decision-makers seeking quantitative evidence from relational database transactions.
My Role & Responsibility
I used MySQL Workbench to query related tables, with filtering, grouping, joins, aggregations, and subqueries, focusing on converting business questions into queries and interpreting returned data.
Process & Execution
Explored the Chinook relational database schema and table relationships in MySQL Workbench.
Formulated SQL queries to investigate sales and purchasing patterns.
Utilized filtering, grouping, joins, aggregations, and subqueries to query related tables.
Executed queries and inspected returned data rows for consistency and relational integrity.
Interpreted the query results to extract clear, data-backed business insights.
Solution & Core Features
A comprehensive SQL analysis querying related tables in MySQL Workbench to answer key business questions regarding invoice patterns, customer purchases, and catalog metrics.
Results & Learnings
Successfully converted key business questions into multi-table SQL queries.
Queried relational tables using joins, groupings, aggregations, and subqueries in MySQL Workbench.
Interpreted query results to provide clear evidence on purchasing and sales patterns.
"How to query multi-table relational databases in MySQL Workbench, translate business inquiries into SQL, and interpret returned data accurately."