Authors:
Yudith Cardinale
1
;
Sonia Guehis
2
and
Marta Rukoz
2
Affiliations:
1
Universidad Simón Bolívar, Venezuela
;
2
Université Paris Nanterre, Université Paris Dauphine, PSL Research University and CNRS, France
Keyword(s):
Big Data Analytic, Analytic Models for Big Data, Analytical Data Management Applications.
Related
Ontology
Subjects/Areas/Topics:
Big Data
;
Data Engineering
;
Data Management and Quality
Abstract:
Analytical data management applications, affected by the explosion of the amount of generated data in the
context of Big Data, are shifting away their analytical databases towards a vast landscape of architectural
solutions combining storage techniques, programming models, languages, and tools. To support users in the
hard task of deciding which Big Data solution is the most appropriate according to their specific requirements,
we propose a generic architecture to classify analytical approaches. We also establish a classification of the
existing query languages, based on the facilities provided to access the big data architectures. Moreover, to
evaluate different solutions, we propose a set of criteria of comparison, such as OLAP support, scalability, and
fault tolerance support. We classify different existing Big Data analytics solutions according to our proposed
generic architecture and qualitatively evaluate them in terms of the criteria of comparison. We illustrate how
o
ur proposed generic architecture can be used to decide which Big Data analytic approach is suitable in the
context of several use cases.
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