Tuplex: robust, efficient analytics when Python rules

Spiegelberg, Leonhard F; Kraska, Tim

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Tuplex: robust, efficient analytics when Python rules

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Author(s)

Spiegelberg, Leonhard F

•

Kraska, Tim

Journal

Proceedings of the VLDB Endowment

Publisher

VLDB Endowment

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Final published version

Abstract

© 2019 VLDB Endowment. Spark became the defacto industry standard as an execution engine for data preparation, cleaning, distributed machine learning, streaming and, warehousing over raw data. However, with the success of Python the landscape is shifting again; there is a strong demand for tools which better integrate with the Python landscape and do not have the impedance mismatch like Spark. In this paper, we demonstrate Tuplex (short for tuples and exceptions), a Pythonnative data preparation framework that allows users to develop and deploy pipelines faster and more robustly while providing bare-metal execution times through code compilation whenever possible.

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Creative Commons Attribution-NonCommercial-NoDerivs License

http://creativecommons.org/licenses/by-nc-nd/4.0/

Persistent DSpace Link

https://hdl.handle.net/1721.1/132284

DOI of Published Version

10.14778/3352063.3352109