Big Data Architectures: A Detailed and Application Oriented Analysis
Godson Koffi Kalipe1, Rajat Kumar Behera2
1Godson Koffi Kalipe, School of Computer Engineering, Kalinga Institute of Industrial Technology, Odisha, India.
2Rajat Kumar Behera, School of Computer Engineering, Kalinga Institute of Industrial Technology, Odisha, India.
Manuscript received on 29 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 2182-2190 | Volume-8 Issue-9, July 2019 | Retrieval Number: F3616048619/19©BEIESP | 10.35940/ijitee.H7179.078919
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Big Data refers to huge amounts of heterogeneous data from both traditional and new sources, growing at a higher rate than ever. Due to their high heterogeneity, it is a challenge to build systems to centrally process and analyze efficiently such data which are internal and external to organizations. A Big data architecture describes the blueprint of a system handling massive volume of data during its storage, processing, analysis and visualization. Several architectures belonging to different categories have been proposed by academia and industry but the field is still lacking benchmarks. Therefore, a detailed analysis of the characteristics of the existing architectures is required in order to ease the choice between architectures for specific use cases or industry requirements. The types of data sources, the hardware requirements, the maximum tolerable latency, the fitment to industry, the amount of data to be handled are some of the factors that need to be considered carefully before making the choice of an architecture of a Big Data system. However, the wrong choice of architecture can result in huge decline for a company reputation and business. This paper reviews the most prominent existing Big Data architectures, their advantages and shortcomings, their hardware requirements, their open source and proprietary software requirements and some of their real-world use cases catering to each industry. The purpose of this body of work is to equip Big Data architects with the necessary resources to make better informed choices to design optimal Big Data systems.
Index Terms: Big Data Architecture, Big Data Architectural Patterns, Big Data Use Cases
Scope of the Article: Big Data