Paper Info
Title
Evaluating SQL-on-Hadoop for Big Data Warehousing on Not-So-Good Hardware
Abstract
Big Data is currently conceptualized as data whose volume, variety or velocity impose significant difficulties in traditional techniques and technologies. Big Data Warehousing is emerging as a new concept for Big Data analytics. In this context, SQL-on-Hadoop systems increased notoriety, providing Structured Query Language (SQL) interfaces and interactive queries on Hadoop. A benchmark based on a denormalized version of the TPC-H is used to compare the performance of Hive on Tez, Spark, Presto and Drill. Some key contributions of this work include: the direct comparison of a vast set of technologies; unlike previous scientific works, SQL-on-Hadoop systems were connected to Hive tables instead of raw files; allow to understand the behaviour of these systems in scenarios with ever-increasing requirements, but not-so-good hardware. Besides these benchmark results, this paper also makes available interesting findings regarding an architecture and infrastructure in SQL-on-Hadoop for Big Data Warehousing, helping practitioners and fostering future research.
Year
DOI
Venue
2017
10.1145/3105831.3105842
IDEAS
Field
DocType
ISBN
Data warehouse,Data science,SQL,Data mining,Architecture,Spark (mathematics),Computer science,Computer hardware,Drill,Big data,Database
Conference
978-1-4503-5220-8
Citations 
PageRank 
References 
4
0.53
18
Authors
7
Name
Order
Citations
PageRank
Maribel Yasmina Santos114635.41
Carlos Costa2389.15
João Galvão381.17
Carina Andrade4233.57
Bruno Augusto Martinho540.53
Francisca Vale Lima681.17
Eduarda Costa791.86