Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/60417

TitleBig data processing tools: An experimental performance evaluation
Author(s)Rodrigues, Mário
Santos, Maribel Yasmina
Bernardino, Jorge
KeywordsBig Data
Big Data analytics
query processing
SQL-on-Hadoop
Issue date2019
PublisherWiley-Blackwell
JournalWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Abstract(s)Big Data is currently a hot topic of research and development across several business areas mainly due to recent innovations in information and communication technologies. One of the main challenges of Big Data relates to how one should efficiently handle massive volumes of complex data. Due to the notorious complexity of the data that can be collected from multiple sources, usually motivated by increasing data volumes gathered at high velocity, efficient processing mechanisms are needed for data analysis purposes. Motivated by the rapid growth in technology, development of tools, and frameworks for Big Data, there is much discussion about Big Data querying tools and, specifically, those that are more appropriated for specific analytical needs. This paper describes and evaluates the following popular Big Data processing tools: Drill, HAWQ, Hive, Impala, Presto, and Spark. An experimental evaluation using the Transaction Processing Council (TPC-H) benchmark is presented and discussed, highlighting the performance of each tool, according to different workloads and query types. This article is categorized under: Technologies > Computer Architectures for Data Mining Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Data Preprocessing Application Areas > Data Mining Software Tools.
TypeArticle
URIhttp://hdl.handle.net/1822/60417
DOI10.1002/widm.1297
ISSN1942-4787
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Artigos em revistas internacionais/Papers in international journals

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