A Comparative Study on Streaming Frameworks for Big Data

Wissem Inoubli (1, 2) , Sabeur Aridhi (3, 2) , Haithem Mezni (4) , Mondher Maddouri (5) , Engelbert Mephu Nguifo (6)

1 LIPAH - Laboratoire d'Informatique, Programmation, Algorithmique et Heuristique
2 CAPSID - Computational Algorithms for Protein Structures and Interactions
3 LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
4 UJ - Université de Jendouba
5 University of Jeddah
6 LIMOS - Laboratoire d'Informatique, de Modélisation et d'optimisation des Systèmes

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 streaming in Big Data, a task referring to the processing of massive volumes of structured/unstructured streaming data. Recently proposed streaming frameworks for Big Data applications help to store, analyze and process the continuously captured data. In this paper, we discuss the challenges of Big Data and we survey existing streaming frameworks for Big Data. We also present an experimental evaluation and a comparative study of the most popular streaming platforms.

Domaines: Base de données [cs.DB]; Calcul parallèle, distribué et partagé [cs.DC]

Wissem Inoubli, Sabeur Aridhi, Haithem Mezni, Mondher Maddouri, Engelbert Mephu Nguifo. A Comparative Study on Streaming Frameworks for Big Data. VLDB 2018 - 44th International Conference on Very Large Data Bases : Workshop LADaS - Latin American Data Science, Aug 2018, Rio de Janeiro, Brazil. pp.1-8. ⟨hal-01835437⟩