End-to-End Data Engineering Project

The world of data engineering is ever-changing, with new tools and technologies emerging regularly. Building an effective analytics platform can be a daunting task, especially if you’re not familiar with all the tools available. How do you turn scattered, complex data into a model that drives insights and decision-making? In this project, I explored best practices such as data modeling, testing, documentation, and version control. I efficiently extract, load, and transform data into a unified, analytics-ready format. This is the construction of a robust data pipeline for a fictional e-commerce company, implementing best practices in data engineering.

Technologies Explored

1. BigQuery
2. dbt
3. Docker
4. Airbyte
5. Dagster

Prerequisites

Ensure you have Python 3 installed. If not, you can download and install it from Python's official website.

Installing

1. Fork the Repository:
   • Click the "Fork" button on the top right corner of this repository.
2. Clone the repository:
   • git clone https://github.com/YOUR_USERNAME/data-engineering.git
   • Note: Replace YOUR_USERNAME with your GitHub username
3. Navigate to the directory:
   • cd data engineering
4. Set Up a Virtual Environment:
   • For Mac:
     • python3 -m venv venv
     • source venv/bin/activate
   • For Windows:
     • python -m venv venv
     • .\venv\Scripts\activate
5. Install Dependencies:
   • pip install -e ".[dev]"