Efficient Analysis of Big Data Using Map Reduce Framework

Siddaraju, Sowmya, Rashmi
2014 International Journal of Recent Development in Engineering and Technology Website: www.ijrdet.com   unpublished
Data now stream from daily life from phones and credit cards and televisions and computers; from the infrastructure of cities from sensor-equipped buildings, trains, buses, planes, bridges, and factories. The data flow so fast that the total accumulation of the past two years is now a zettabyte. This huge volume of data is known as big data. Big Data refers to technologies and initiatives that involve data that is too diverse, fast-changing or massive for conventional technologies, skills and
more » ... frastructure to address efficiently. Said differently, the volume, velocity or variety of data is too great. The volume of data with the speed it is generated makes it difficult for the current computing infrastructure to handle big data. To overcome this drawback, big data processing can be performed through a programming paradigm known as MapReduce. Typical, implementation of the mapReduce paradigm requires networked attached storage and parallel processing. Hadoop and HDFS by apache is widely used for storing and managing big data. In this research paper the authors suggest various methods for catering to the problems in hand through MapReduce framework over HDFS. MapReduce technique has been studied at in this paper which is needed for implementing Big Data analysis using HDFS.
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