An experimental survey on big data frameworks
Résumé
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 with several representative
batch and iterative workloads. This survey is
concluded with a presentation of best practices related to
the use of studied frameworks in several application domains
such as machine learning, graph processing and real-world
applications.