An Experimental Survey on Big Data Frameworks

TL;DR

This paper surveys and experimentally evaluates popular Big Data frameworks, discussing challenges, best practices, and their application in domains like machine learning and graph processing.

Contribution

It provides a comprehensive survey and experimental comparison of Big Data frameworks, highlighting best practices for various application domains.

Findings

Identifies key challenges in Big Data processing.

Provides comparative performance analysis of frameworks.

Recommends best practices for different applications.

Abstract

Recently, increasingly large amounts of data are generated from a variety of sources. Existing data processing technologies are not suitable to cope with the huge amounts of generated data. Yet, many research works focus on Big Data, a buzzword referring to the processing of massive volumes of (unstructured) data. Recently proposed frameworks for Big Data applications help to store, analyze and process the data. In this paper, we discuss the challenges of Big Data and we survey existing Big Data frameworks. We also present an experimental evaluation and a comparative study of the most popular Big Data frameworks. This survey is concluded with a presentation of best practices related to the use of the studied frameworks in several application domains such as machine learning, graph processing and real-world applications.