Title

Usage of Hadoop and Microsoft Cloud in Big Data Analytics

Document Type

Article

Publication Date

9-2018

Journal Title

AIMS International

Volume

12

Issue

3

Abstract

This paper examines data analytics and how changes in the field have given rise to several new programs. This paper also provides discussion on these programs and how the programs approach large data analytics and allay the concerns of ever-growing data files. After examining the basics, (Hadoop and Cloud services) further exploration develops by looking at complementary software and programs that facilitate Hadoop in the Cloud. Services such as Amazon S3, Apache Drill, Cloudera, and Amazon EMR work in facilitation with Hadoop. Amazon S3 can be used to store data in non-SQL database. Amazon S3 storage allows for bucket versioning and elasticity. Apache Drill combines non-search Query Language and database file systems. Cloudera demonstrates a superior elastic platform, allowing infrastructures to be leveraged With Cloudera's current structure, flexibility enhances the businesses' ability to minimize cloud lock-in. Amazon EMR is an Amazon Web service toll that allows for big data processing and analysis. Amazon's EMR service allows one to run Hadoop without any software installation on a machine. The software grew in popularity in response to the increasing overall demands on big data analytics and the need for programs which could handle massive data files. This changed how computational finance was done; the availability of a new and versatile tool is apt to reframe at least a portion of the discussions around calculation and application. Lastly, the paper examines the benefits and drawbacks of these solutions overall in order to give a clear picture of the program landscape. In conclusion, the benefits outperform the drawbacks and that concerns can be addressed via a mixed software solution, which can be adapted to the needs of any business. Hadoop and Microsoft Cloud are therefore very beneficial to the analytics field and have been increasing the amount of data being analyzed.

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