Big Data Analytics With Service-Oriented Architecture

Big Data Analytics With Service-Oriented Architecture

Triparna Mukherjee (St. Xavier's College, India) and Asoke Nath (St. Xavier's College, India)
Copyright: © 2017 |Pages: 19
DOI: 10.4018/978-1-5225-2157-0.ch015
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Abstract

This chapter focuses on Big Data and its relation with Service-Oriented Architecture. We start with the introduction to Big Data Trends in recent times, how data explosion is not only faced by web and retail networks but also the enterprises. The notorious “V's” – Variety, volume, velocity and value can cause a lot of trouble. We emphasize on the fact that Big Data is much more than just size, the problem that we face today is neither the amount of data that is created nor its consumption, but the analysis of all those data. In our next step, we describe what service-oriented architecture is and how SOA can efficiently handle the increasingly massive amount of transactions. Next, we focus on the main purpose of SOA here is to meaningfully interoperate, trade, and reuse data between IT systems and trading partners. Using this Big Data scenario, we investigate the integration of Services with new capabilities of Enterprise Architectures and Management. This has had varying success but it remains the dominant mode for data integration as data can be managed with higher flexibility.
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Introduction To Big Data

In financially indeterminate times the consumer as well as the producer is faced with a large number of choices of different kinds, not only do we consider historical information but we also make reasoned choices among alternatives that are statistically desirable. Most of the big business and corporate sectors are now appreciating the use of Big Data helps them to take decision in right time. The term ―big data‖ was invented while addressing one of the most prominent problems of handling huge amount of structured or unstructured data which is size. In short the term big data is applies to information that can‘t be processed with traditional processes or tools. While almost all industries today have access to a high volume of information, it is evident that most of it is sitting in its raw form in an unstructured or semi-structured format and hence tends to confuse people whether it is actually useful to keep and analyze or not. In the present business scenario it is found that the access and processing data is going very fast. Big data analytics basically deals with how to turn that nebulous, vast, fast-flowing mass of ―Big Data into decidedly valuable acumens, actions and outcomes. A new area of computer science has been developed called data Science which deals with preparation, collection, analysis, virtualization, preservation and management of large volume of collections of information.

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