Kedro is an open-source Python framework designed to facilitate the development of robust data pipelines. It offers a high level of flexibility and scalability, making it ideal for managing complex workflows in data science and machine learning projects. By providing a standardized approach to structuring code and data, Kedro enables teams to collaborate effectively and maintain a clear understanding of the pipeline's architecture.
In the realm of Frameworks (Full Stack), Kedro falls under the category of data pipeline frameworks. It focuses on streamlining the process of building, deploying, and managing data pipelines that are crucial for extracting insights from large datasets. Kedro's emphasis on modularity and reproducibility sets it apart, allowing users to easily experiment with different data processing techniques and iterate on their pipelines with confidence.
Kedro was founded in 2019 by QuantumBlack, a data science consultancy that aimed to address the challenges faced by data professionals in structuring and managing their projects effectively. Drawing on their extensive experience in the field, QuantumBlack developed Kedro to provide a comprehensive solution for building scalable and maintainable data pipelines. Since its inception, Kedro has gained traction among data engineering and data science teams looking to streamline their workflow and enhance collaboration.
In terms of current market share, Kedro has been steadily gaining popularity within the data engineering and data science communities. Its user-friendly interface, extensive documentation, and active community support have contributed to its growing adoption rate. As organizations continue to recognize the importance of efficient data pipeline management, Kedro is poised to experience further growth in the foreseeable future, cementing its position as a leading framework in the data science ecosystem.
