A Grammar for Reproducible and Painless Extract-Transform-Load Operations on Medium Data

TL;DR

This paper introduces a framework that combines R and SQL to facilitate reproducible and efficient extract-transform-load operations on medium-sized data sets, addressing a key challenge in data science workflows.

Contribution

It presents a novel, pipeable framework integrating R and SQL for reproducible data processing of medium data, simplifying workflows and enhancing scientific rigor.

Findings

Enables reproducible ETL workflows in R using SQL.

Reduces complexity of handling medium data sets.

Improves transparency and peer-review of data analysis.

Abstract

Many interesting data sets 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 won't fit comfortably on its hard disk. In the coming years, data sets 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.