SQL Database Normalization with Python

Introduction

This project focuses on demonstrating SQL database normalization techniques, up to the third normal form (4NF), using Python. The goal is to organize a database efficiently to reduce redundancy and improve data integrity. It includes automated data generation for a sales transaction dataset, utilizing the Faker Python library. Faker creates realistic sales transactions, simulating customer, product, and order data.

An automation pipeline is also set up in the .github/workflows directory using GitHub Actions. This pipeline automates the process of generating data and updating the database with each new commit, ensuring fresh data is consistently available for normalization scripts.

Table of Contents

• Introduction
• Installation
• Usage
• Features
• Dependencies
• Contributors
• License

Installation

1. Clone the repository:

   git clone https://github.com/AgVicCodes/2_sql_database_normalization_with_python.git

2. Install the dependencies:

   pip install -r requirements.txt

Usage

1. Generate sample data using data_generator.py.
2. Load data into the database with load_to_sql.py.
3. Run the SQL normalization scripts like normalisation.sql.

Features

• Data generation and loading scripts.
• SQL scripts for normalization up to 4NF.
• Sample database schema and data included.

Dependencies

• Python
• SQL

Contributors

This project is maintained by AgVicCodes.

License

This project is licensed under the MIT License.