Developing Maintainable Data Pipelines With Jupyter and Ploomber

Abstract

Jupyter is a prevalent tool among data scientists due to its flexibility, allowing users to interleave code, text, and images. However, writing notebooks that follow software engineering best practices is notoriously difficult: reproducibility, version control, modularization, and collaboration are all challenging to achieve when working with notebooks. Ploomber is an open-source library that allows users to adhere to software best practices while keeping the power of Jupyter.

GitHub: https://github.com/ploomber/ploomber

Bio

Eduardo is interested in developing tools to deliver reliable data products. Towards that end, he created Ploomber, an open-source Python library to compose production-ready data workflows. Eduardo holds an M.S in Data Science from Columbia University, where he took part in Computational Neuroscience research. Eduardo started his Data Science career in 2015 at the Center for Data Science and Public Policy at The University of Chicago.

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