DataForge: Simplify Data Population in Your Database

DataForge is a Python tool designed to assist in generating and inserting realistic test data into your database. It aims to simplify tasks like detecting foreign key relations, with a focus on user-friendly design and efficiency. it is a valuable addition to any toolkit.

🚀 Key Features

Automated Data Generation

DataForge simplifies the data population process by automatically detecting foreign key relations and inserting data that respects these relations. No more manual data entry or complex scripting.

Customizable Configuration

Tailor DataForge to meet your specific needs. Adjust the number of rows to insert, select tables to fill, and customize data generation instructions to match your database schema.

Data Visualization

Gain insights into your data structure. DataForge provides a visualization of your database's foreign relations graph after data insertion, making it easier to understand your data model.

📖 How to Use

Getting started with DataForge is a breeze:

1. Install Dependencies: Begin by installing the required dependencies. Run the following command:

pip install -r requirements.txt

2. Database Connection: Set up your database connection details in the .env file following the example in .env.sample.

3. Configuration: Customize DataForge's behavior by configuring the data.py file. Here, you can specify the number of rows to insert, exclude tables from data insertion, and more.

4. Run DataForge: Execute main.py to start populating your database effortlessly.

⚙️ Configuration

DataForge's flexibility lies in its configuration options in data.py. You can fine-tune the tool to your precise requirements:

• number_of_fields: Specify the number of rows to insert into the database.

• excluded_tables: Define a list of tables to exclude from data insertion.

• tables_to_fill: Select specific tables for data insertion; leave it empty to fill all tables.

• graph: Opt to display the database's foreign relations graph after data insertion.

• field: Configure how columns are identified and filled with data.

  Field	Description
  name	The name of the field.
  type	The type of the field.
  table	The name of the table where the field is located.
  generator	Generator function to be used for data insertion.

Feel free to adjust these configurations to match your unique use case.

🛠️ Prerequisites

• Python 3.11.3
• MySQL database server

📦 Dependencies

DataForge relies on several essential Python libraries:

• SQLAlchemy 2.0.20
• mysql-connector-python 8.1.0
• Faker 18.9.0
• matplotlib 3.7.2
• networkx 3.1
• python-decouple 3.8
• rich 13.5.2
• SQLAlchemy-Utils 0.41.1

📞 Support

If you have any questions or need assistance with using DataForge, don't hesitate to contact me at [email protected]. Your feedback is invaluable as I continue to enhance this tool.

Thank you for choosing DataForge! I hope it simplifies your data population tasks and proves to be a valuable asset in your development journey.