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The "Missing Money Matters" project is an engaging SQL and NoSQL training exercise where I played the role of a data analyst for WSDA Music. Tasked with investigating financial discrepancies in the company's records, I utilized SQLite and MongoDB to analyze and manipulate data.

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Missing-Money-Matters

The "Missing Money Matters" project is an engaging SQL and NoSQL training exercise where I played the role of a data analyst for WSDA Music. Tasked with investigating financial discrepancies in the company's records, I utilized SQLite and MongoDB to analyze and manipulate data.

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Overview

This repository contains my solutions to the "Missing Money Matters" project, a part of the SQL Essential Training series by LinkedIn Learning, using both SQL (SQLite) and NoSQL (MongoDB) databases.

Challenge Description

The project involves investigating financial discrepancies in the records of WSDA Music between 2011 and 2012. It required applying SQL and NoSQL techniques to analyze data and identify potential suspects.

SQL Solution

SQLite Implementation

  • Setup and queries for SQLite database.
  • Focused on relational data structures and complex SQL queries.

NoSQL Solution

MongoDB Implementation

  • Setup and queries for MongoDB database using pymongo library in Python.
  • Emphasized handling document-oriented and unstructured data.

Data Sets

The project involved data from Customers, Employees, and Invoices Tables / Collections from the WSDA Music Database, adapted for both relational and non-relational databases.

Setup and Running

  1. Clone this repo.
  2. Set up SQLite and MongoDB environments.
  3. Execute SQL and NoSQL scripts as per the instructions.

Contributions

Feel free to fork and contribute to this project. Enhancements and additional challenges are welcome.

Acknowledgments

Special thanks to Walter Shields for the foundational project design.

About

The "Missing Money Matters" project is an engaging SQL and NoSQL training exercise where I played the role of a data analyst for WSDA Music. Tasked with investigating financial discrepancies in the company's records, I utilized SQLite and MongoDB to analyze and manipulate data.

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