spark_airport
is a Python-based data processing project that leverages Apache Spark for efficient and scalable
analysis of airport-related data. The project aims to provide a framework for processing and analyzing large datasets
related to airport operations, flights, and other relevant information.
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Apache Spark Integration: Harness the power of Apache Spark for distributed data processing, enabling parallelized and efficient analysis of large datasets.
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Airport Data Processing: The project includes functionality to process and analyze diverse airport-related datasets, including information about flights, airports, and relevant metrics.
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Scalable and Parallel Processing: Benefit from the scalability and parallel processing capabilities of Apache Spark, allowing the project to handle substantial amounts of data with ease.
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Data Exploration and Analysis: Explore and analyze airport data using Spark's rich set of data manipulation and analysis tools, facilitating insights and decision-making.
- Python 3.x
- Apache Spark (installation instructions can be found on the official Apache Spark website)
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Clone the repository:
git clone https://github.com/IvanSedinkin/spark_airport.git
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Install the required dependencies:
pip install -r requirements.txt
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Set up Apache Spark according to your environment.
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Ensure Apache Spark is running.
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Navigate to the project directory:
cd spark_airport
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Execute the main script:
python main.py
Contributions are welcome! Feel free to open issues, submit pull requests, or provide feedback.
- Fork the repository.
- Create a new branch: git checkout -b feature/new-feature.
- Make your changes and commit them: git commit -m 'Add new feature'.
- Push to the branch: git push origin feature/new-feature.
- Open a pull request.
For any inquiries or discussions, please reach out to the project owner: Ivan Sedinkin
GitHub: IvanSedinkin
Email: [email protected]