Data Provenance and Reproducibility with Pachyderm

Versioning isn't just for source code. Being able to track changes to data is critical for answering questions about data provenance, quality, and reproducibility. Daniel Whitenack joins me this week to talk about these concepts and share his work on Pachyderm. Pachyderm is an open source containerized data lake.

During the show, Daniel mentioned the Gopher Data Science github repo as a great resource for any data scientists interested in the Go language. Although we didn't mention it, Daniel also did an interesting analysis on the 2016 world chess championship that complements our recent episode on chess well. You can find that post here

Supplemental music is Lee Rosevere's Let's Start at the Beginning.

Periscope Data

Thanks to Periscope Data for sponsoring this episode. More about them at

Daniel Whitenack

Daniel Whitenack


Daniel Whitenack has a PhD in computation physics from Purdue University. He's worked in a diverse set of industries developing data science applications and is a frequent speaker at conferences. He's the maintainer of the Go kernel for Project Jupyter and is presently a data scientist at lead developer advocate at Pachyderm Inc. Daniel also teaches corporate and public data science and engineering courses with Arden Labs.
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