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arXiv · 2004.12929

Data Engineering for Data Analytics: A Classification of the Issues, and Case Studies

Alfredo Nazabal·Christopher K. I. Williams·Giovanni Colavizza·Camila Rangel Smith·Angus Williams
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Abstract

Consider the situation where a data analyst wishes to carry out an analysis on a given dataset. It is widely recognized that most of the analyst's time will be taken up with \emph{data engineering} tasks such as acquiring, understanding, cleaning and preparing the data. In this paper we provide a description and classification of such tasks into high-levels groups, namely data organization, data quality and feature engineering. We also make available four datasets and example analyses that exhibit a wide variety of these problems, to help encourage the development of tools and techniques to help reduce this burden and push forward research towards the automation or semi-automation of the data engineering process.

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Alfredo Nazabal, Christopher K. I. Williams, Giovanni Colavizza, Camila Rangel Smith, Angus Williams. 2020-04-27. Data Engineering for Data Analytics: A Classification of the Issues, and Case Studies. https://arxiv.org/abs/2004.12929

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