Welcome to Data-Engineering-Projects, a comprehensive repository dedicated to housing innovative and scalable data engineering solutions.
This repository is a curated collection of projects and tools that exemplify best practices in data engineering. It serves as a resource for data professionals seeking to enhance their data infrastructure, optimize data pipelines, and implement cutting-edge data processing techniques.
Each project within this repository is self-contained with its own set of instructions, documentation, and necessary scripts or code.
- Project 1: Retail Data Pipeline - AirFlow
- Project 2: Uber Data Pipeline - Mage
The projects in this repository leverage a variety of technologies, including:
- Apache Spark
- Apache Airflow
- Amazon Redshift
- Google BigQuery
- SnowFlake
- Docker
- Mage
Instructions on how to install and configure the necessary environment or dependencies for the projects.
# Example installation code
pip install -r requirements.txtExamples of how to use the projects or tools within this repository.
# Example usage code
python project_1/main.pyWe welcome contributions from the data engineering community. Please read our CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests.
This project is licensed under the MIT License - see the LICENSE.md file for details.
For any inquiries or contributions, please open an issue or contact the repository maintainers.
Thank you for visiting Data-Engineering-Projects. We hope this repository empowers you to build robust and efficient data solutions.