Study and Analysis of Big Data with MapReduce Framework
K. Rama Krishna Reddy1, B.G. Obula Reddy2
1K. Rama Krishna Reddy, Associate Professor, Department of CSE, Malla Reddy Engineering College (A), Hyderabad (Telangana), India.
2B.G. Obula Reddy, Associate Professor, Department of CSE, Malla Reddy Engineering College (A), Hyderabad (Telangana), India.
Manuscript received on 06 February 2019 | Revised Manuscript received on 28 March 2019 | Manuscript Published on 28 April 2019 | PP: 72-74 | Volume-7 Issue-5C February 2019 | Retrieval Number: E10190275C19/19©BEIESP
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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: Exponential growth in data has been observed in recent years. This huge amount of data has caused a new kind of problem. Existing RDBMS systems cannot handle large data or they are not effective in managing them. Major Big Data problems are storage and handling. Hadoop is displayed in storage and processing solutions in the form of HDFS (Hadoop Distributed File System) and MapReduce. Traditional systems are not intended for Big Data processing, and they can also process structured data. The financial sector is one of the challenges in Big Data. In this work, unstructured data is processed by Hadoop MapReduce. An effective processing of unstructured data is analyzed and explained.
Keywords: Big Data, Hadoop, HDFS, MapReduce.
Scope of the Article: Patterns and Frameworks