A scalable framework for data input and output operations in Spark applications
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Project description
PyData I/O
Data I/O is an open source project that provides a flexible and scalable framework for data input and output operations in Spark applications. It offers a set of powerful tools and abstractions to simplify and streamline data processing pipelines.
Features
- Easy-to-use API for defining data processors and transformations
- Seamless integration with popular data storage systems and formats
- Support for batch and streaming data processing
- Extensible architecture for custom data processors and pipelines
- Scalable and fault-tolerant processing using Apache Spark
- Open to make use of python ML models ecosystem (sklearn, xgboost, pytorch...)
Getting Started
To get started with PyData I/O, please refer to the documentation for installation instructions, usage examples, and API references.
Issues and Support
If you encounter any issues or require support, please create a new issue on the GitHub repository.
Contribution
Contributions to Data I/O are welcome! To contribute, please follow the guidelines outlined in our contribution guide.
License
This project is licensed under the Apache License 2.0 license. See the LICENSE file for more information.
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