Pasteur is a library for performing privacy-aware end-to-end data synthesis. Gather your raw data and preprocess, synthesize, and evaluate it within a single project. Use the tools you're familiar with: numpy, pandas, scikit-learn, scipy or any other. When your dataset grows, scale to out-of-core data by using Pasteur's parallelization and partitioning primitives, without code changes or using different libraries.

This use of Kedro is quite creative: https://github.com/pasteur-dev/pasteur

pasteur new --starter=pasteur

🙈

I wish I could say that I'm a master 🕵🏾‍♀️ but all I do is check which publicly available projects were created with Kedro: https://github.com/kedro-org/kedro/network/dependents 1,378 projects and counting 🚀

what’s also super interesting is that they don’t depend on a version of Kedro, they’ve used a practice called “vendoring” where they bundle what they need internally as source code rather than tie themselves to a specific version https://github.com/pasteur-dev/pasteur/tree/master/src/pasteur