A Grammar for Reproducible and Painless Extract-Transform-Load Operations on Medium Data
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| Authors | Benjamin S. Baumer |
| Journal/Conference Name | arXiv |
| Paper Category | Other |
| Paper Abstract | ABSTRACTMany interesting datasets available on the Internet are of a medium size—too big to fit into a personal computer’s memory, but not so large that they would not fit comfortably on its hard disk. In the coming years, datasets of this magnitude will inform vital research in a wide array of application domains. However, due to a variety of constraints they are cumbersome to ingest, wrangle, analyze, and share in a reproducible fashion. These obstructions hamper thorough peer-review and thus disrupt the forward progress of science. We propose a predictable and pipeable framework for R (the state-of-the-art statistical computing environment) that leverages SQL (the venerable database architecture and query language) to make reproducible research on medium data a painless reality. Supplementary material for this article is available online. |
| Date of publication | 2017 |
| Code Programming Language | R |
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