What is chinook_and_sales-data_sql-and-data-queries?

This repository is a portfolio project by Lynn Tiffany that shows off SQL skills using two separate datasets, rather than being a piece of software someone installs and runs. It does not contain an application, only documentation describing the SQL queries and analysis the author performed, so it works best as an example to read rather than a tool to use. The first part uses the Chinook sample database, a commonly used practice dataset built around a fictional digital music store, with tables for artists, albums, tracks, customers, invoices, employees, genres, and playlists. The author imported this data into MySQL and wrote a range of SQL queries against it, starting with basic filtering and sorting, moving through joins and subqueries, and ending with window functions like ROW_NUMBER and RANK. These queries were used to answer business style questions, such as which tracks generated the most revenue, who the highest spending customers were, and how revenue changed over time. The README also mentions exploring basic query optimization ideas, including indexing and the EXPLAIN statement. The second part applies the same kind of analysis to a separate, unspecified sales dataset, again using MySQL Workbench. The questions answered here are similar in spirit: which products generate the most revenue, who the top customers are, how revenue trends over time, and what patterns show up in customer purchasing behavior. The README describes the same general toolkit, aggregation functions, joins, subqueries, and window functions, applied to this second dataset. Overall, this repository is best understood as a learning and demonstration project meant to showcase relational database analysis and business focused SQL querying skills, built with MySQL Workbench, SQL itself, and Git for version control. No license is listed in the README, and the repository does not describe any code beyond the SQL queries themselves.