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This Project is about performing an Extract, Transform and Load Pipeline on Crowdfunding data using Python and Pandas. After transforming the data, we created four CSV files and used the CSV file data to create an ERD and a table schema. Finally, uploaded the CSV file data into a Postgres database.

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malihaspk/ETL-Crowd-Funding-Project

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Crowdfunding_ETL

This repository contains solutions to the Project 2 Crowdfunding ETL challenge

Part 1: ETL Mini Project

  • In this part of the project the crowdfunding.xslx and contacts.xslx files were imported to extract, process and clean data to transform to a CSV ready to load using pgAdmin
  • This is performed via a jupyter notebook file 'ETL_Mini_Project.ipynb'
  • Outputs from this are four .csv files as follows: 'campaign.csv', 'contacts.csv', 'category.csv' and 'subcategory.csv'

Part 2: Load CSV files using pgAdmin

  • An ERD was generated using QuickDBD. The commands for this are included in the 'QuickDBD_ERD.txt' file. The exported PNG file is below.
  • A schema for the four entities is saved as 'crowdfunding_db_schema.sql'
  • Four CSV files were imported into the tables and correct import verified using the SELECT * FROM commands

ERD Diagram

References

QuickDBD: https://www.quickdatabasediagrams.com/

About

This Project is about performing an Extract, Transform and Load Pipeline on Crowdfunding data using Python and Pandas. After transforming the data, we created four CSV files and used the CSV file data to create an ERD and a table schema. Finally, uploaded the CSV file data into a Postgres database.

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