Technical Report: On the Usability of Hadoop MapReduce, Apache Spark & Apache Flink for Data Science

TL;DR

This study compares Hadoop MapReduce, Apache Spark, and Apache Flink in terms of usability for data science, finding Spark and Flink more user-friendly than MapReduce, with no significant difference between Spark and Flink.

Contribution

It provides an empirical usability comparison of three major data processing platforms specifically for data science tasks.

Findings

Spark and Flink are preferred over MapReduce.

No significant difference in preference or development time between Spark and Flink.

The study explores factors influencing platform effectiveness 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. This 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. This study starts an exploration of the factors that make big data platforms more - or less - effective for users in data science.