Paper Info
Title
Technical Report: On the Usability of Hadoop MapReduce, Apache Spark & Apache Flink for Data Science.
Abstract
Distributed data processing platforms for cloud computing are important tools for large-scale data analytics. Apache Hadoop MapReduce has become the de facto standard in this space, though its programming interface is relatively low-level, requiring many implementation steps even for simple analysis tasks. has led to the development of advanced dataflow oriented platforms, most prominently Apache Spark and Apache Flink. Those platforms not only aim to improve performance through improved in-memory processing, but in particular provide built-in high-level data processing functionality, such as filtering and join operators, which should make data analysis tasks easier to develop than with plain Hadoop MapReduce. But is this indeed the case? This paper compares three prominent distributed data processing platforms: Apache Hadoop MapReduce; Apache Spark; and Apache Flink, from a usability perspective. We report on the design, execution and results of a usability study with a cohort of masters students, who were learning and working with all three platforms in order to solve different use cases set in a data science context. Our findings show that Spark and Flink are preferred platforms over MapReduce. Among participants, there was no significant difference in perceived preference or development time between both Spark and Flink as platforms for batch-oriented big data analysis. study starts an exploration of the factors that make big data platforms more - or less - effective for users in data science.
Year
Venue
Field
2018
arXiv: Distributed, Parallel, and Cluster Computing
Data science,De facto standard,Use case,Spark (mathematics),Data analysis,Computer science,Usability,Dataflow,Big data,Cloud computing
DocType
Volume
Citations 
Journal
abs/1803.10836
0
PageRank 
References 
Authors
0.34
10
3
Name
Order
Citations
PageRank
Bilal Akil100.34
Ying Zhou21069.32
Uwe Röhm330831.42