Apache Airflow, ETL, Python, Spark
This repository contains a collection of ETL (Extract, Transform, Load) projects implemented using Airflow, Python, and PySpark. These projects aim to provide scalable and efficient data processing and transformation pipelines.
The ETL Projects repository showcases the power of Airflow, Python, and PySpark in building end-to-end data processing workflows. Each project focuses on different aspects of ETL, including data extraction from various sources, transformation using PySpark, and loading into target destinations. The projects aim to demonstrate best practices for designing scalable and fault-tolerant ETL pipelines.
Before getting started with the projects, make sure you have the following prerequisites:
- Basic understanding of ETL concepts and data processing workflows.
- Knowledge of Python programming and PySpark.
- Familiarity with Airflow and its concepts.
To begin working with the projects, follow these steps:
- Clone the repository to your local machine.
- Install the required dependencies mentioned in the project-specific README files.
- Set up the necessary data sources and connections, such as databases or APIs.
- Configure Airflow to connect with your local or remote Airflow instance.
- Explore the individual project folders for more detailed instructions.
To use a specific project, navigate to the project's folder and follow the instructions provided in the project's README file. This will guide you through setting up the project, configuring data sources, running the ETL pipeline, and monitoring the workflow using Airflow.
Contributions to the ETL Projects repository are welcome! If you have any improvements, bug fixes, or new project ideas, feel free to submit a pull request. Make sure to follow the existing code style and provide clear documentation for your changes.
Happy ETL processing with Airflow, Python, and PySpark! If you have any questions or need further assistance, please reach out to pratikdomadiya123@gmail.com.