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Wendelin (2014 2019)

The Wendelin project was a bold endeavor to create a superior open-source big data engine.

The Wendelin project aimed to be an amalgamation of scikit-learn’s machine learning capabilities and NEO’s distributed storage framework. This hybrid nature offered the best of both worlds—powerful analytics from scikit-learn and the scalability of NEO—enabling the handling of large data sets that required out-of-core processing. Wendelin was not just another analytics engine; it was tailored to make the most out of the pythonic ecosystem of Numpy technologies.

While Wendelin had broad applicability, its immediate focus was on industrial big data and video processing. Whether it was predictive maintenance for machinery, intrusion detection systems, or energy consumption forecasting, Wendelin was equipped to handle it all. And thanks to its compatibility with other Numpy-based libraries like OpenCV and Pandas, its capabilities extended into diverse fields such as finance and media.

But Wendelin was not merely an academic exercise; it was designed for real-world impact. The project actively sought business applications and encouraged industrial partnerships. Unlike other open-source projects that relied heavily on venture capital and were vulnerable to hostile takeovers, Wendelin had envisioned a more sustainable model for long-term R&D. Businesses could extend Wendelin with both open-source and proprietary components to cater to specific vertical markets in big data.

Page last modified: 2024-03-08 14:39:17