Koheesio, named after the Finnish word for cohesion, is a robust Python framework for building efficient data pipelines. It promotes modularity and collaboration, enabling the creation of complex pipelines from simple, reusable components.
An overview of data pipelines structured
Koheesio is structured to promote modularity and collaboration while enabling developers to create complex pipelines from simple and reusable components. The framework is versatile and supports multiple implementations, seamlessly working with various data processing libraries or frameworks, ensuring that any data processing task can be handled efficiently, regardless of the underlying technology or data scale.
Core Components of Koheesio
Step: The fundamental unit of work in Koheesio, operating as a single operation in a data pipeline. Steps take inputs and produce outputs, creating building blocks for more complex processes.
Context: This class configures the environment for a task, sharing variables across tasks and adapting task behavior based on the environment.
Logger: A class for logging messages at different levels, ensuring streamlined debugging and traceability.
Koheesio introduces strong typing, data validation, and settings management through Pydantic, promoting high type safety and structured configurations within pipeline components. This structured approach ensures predictable pipeline execution, built upon a foundation of well-tested code and a rich feature set, making Koheesio an excellent choice for developers and organizations focused on building robust and adaptable data pipelines.
What Sets Koheesio Apart?
Koheesio encapsulates extensive data engineering expertise, fostering a collaborative and innovative community. Unlike other similar libraries, Koheesio focuses explicitly on data pipelines, integrating closely with PySpark, and targeting tasks like data transformation, ETL jobs, data validation, and large-scale data processing. This focus allows it to offer a wide variety of features, including readers, writers, and transformations suitable for any type of data processing. Instead of competing with similar libraries, Koheesio aims for integration and utility across various scenarios.
Installation and Usage
You can install Koheesio using popular package management tools such as pip, Hatch, or poetry:
To install using `pip`, run:
```bash
pip install koheesioUsing `Hatch`, add Koheesio to your `pyproject.toml`.
For `poetry`:
```bash
poetry add koheesio==<desired_version>
Koheesio also provides various additional features and integrations, requiring extra dependencies that can be added as needed:Box Integration: Available through `koheesio.steps.integration.box`.
SFTP Integration: Available through `koheesio.steps.integration.spark.sftp`.
Contributing to Koheesio
Koheesio encourages contributions from the community to foster collaboration and innovation.
Contributing Steps:
Code Standards: Ensure your code passes pylint, black, and mypy checks by running `make check`. No errors or warnings should be reported.
Testing: Use `pytest` for testing, and ensure all tests pass by running `make test`.
Release Process: Frequent releases are aimed at, with admin developers creating new releases on GitHub and publishing to PyPI regularly.
For more details, refer to the contribution guidelines. Koheesio adheres to Nike's Code of Conduct and Individual Contributor License Agreement.
Remember these 3 key ideas for your startup:
Modularity and Versatility: With Koheesio's modular design, you can build complex data pipelines from reusable components, making it easier to adapt and scale your projects.
Community and Collaboration: Embrace the collaborative community fostered by Koheesio to innovate and improve your data engineering tasks continuously.
Robust Features and Integration: Take advantage of Koheesio's rich features and its seamless integration with various data processing libraries to enhance your data pipeline efficiency.
Explore more about how Koheesio can transform your data handling processes and tap into the future of efficient data pipelines with ease.
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