ETL Pipeline – IBM Data Engineering Foundations

This project was developed as part of the IBM Data Engineering Foundations course. It demonstrates a complete ETL (Extract, Transform, Load) process in Python using World Bank GDP data.

📌 Project Overview

Extract: Load raw GDP data from a CSV file.

Transform:

Rename and normalize columns

Handle missing values

Convert currencies and standardize values

Aggregate GDP data by country

Load: Save the processed dataset into a SQLite database for structured storage and analysis.

🛠️ Technologies Used

Python 3

Jupyter Notebook

Pandas

NumPy

SQLite3

📂 Project Structure ├── ETL.ipynb # Jupyter Notebook containing the ETL pipeline ├── data.csv # Raw GDP dataset (source) ├── etl.db # SQLite database with cleaned data (output) └── README.md # Project documentation

🚀 How to Run

Clone the repository:

git clone https://github.com/alopezmoreira1989/IBM_ETL_Proyect.git cd IBM_ETL_Proyect

Install dependencies:

pip install pandas numpy

Open the notebook:

jupyter notebook ETL.ipynb

Run all cells to execute the ETL pipeline.

📖 Learning Outcomes

Built an end-to-end ETL pipeline with Python

Gained hands-on experience in data wrangling and cleaning

Learned how to store structured data in a relational database

Practiced data engineering best practices with real-world data

👨‍💻 Author

Created by Alejandro López Moreira during the IBM Data Engineering Foundations course.