Treffer: Tuplex: Robust, Efficient Analytics When Python Rules

Title:
Tuplex: Robust, Efficient Analytics When Python Rules
Authors:
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Spiegelberg, Leonhard F, Kraska, Tim
Source:
VLDB Endowment
Publisher Information:
VLDB Endowment 2021-12-17T16:25:20Z 2021-09-20T18:21:39Z 2021-12-17T16:25:20Z 2019 2021-01-11T16:52:56Z
Document Type:
E-Ressource Electronic Resource
Index Terms:
Article, http://purl.org/eprint/type/ConferencePaper
URL:
https://hdl.handle.net/1721.1/132284.2
Availability:
Open access content. Open access content
Creative Commons Attribution-NonCommercial-NoDerivs License
http://creativecommons.org/licenses/by-nc-nd/4.0
Note:
application/octet-stream
English
Other Numbers:
MYG oai:dspace.mit.edu:1721.1/132284.2
1342471511
Contributing Source:
MASSACHUSETTS INST OF TECHNOL LIBRS
From OAIster®, provided by the OCLC Cooperative.
Accession Number:
edsoai.on1342471511
Database:
OAIster

Weitere Informationen

© 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.