Crowdfunding data | Extract, Transform, and Load (ETL)

The ETL process was implemented to manipulate dataframes to create csv files into schemas and relationship tables from a Crowdfunding-related excel dataset.

Technologies Applied

Python, Jupyter Lab, Pandas, PostgreSQL, and QuickDBD were the platforms used to implement the ETL Process on Crowdfunding dataset.

• Data was extracted and dataframes were created from the Crowdfunding dataset in Jupyter lab
• The dataframes were converted to csv files and pushed/stored locally into a repository resource folder
• The csv files were exported into Quick DBD and Postgres SQL/Pg4Admin databases where relationship tables and schemas were created, respectively

Please see the attached csv, sql, and ERD diagram files within the folders located above this ReadMe.

Authors:

• Avis Randle
• Irina Tenis