Big Data Using Map Reduce and Hadoop Expertise

Abstract

Today, the data is not only produced by people, but massive data is generated by machines also and it betters human generated data[1]. This data is spread across different places, in diverse formats, in large volumes ranging from Gigabytes to Terabytes, Petabytes, and exabytes. In unlike areas of expertise, data is being generated at different speeds. A few examples include stock exchange data, chirrups on Twitter, status updates/likes/shares on Facebook, data from sensors, images from medical devices, surveillance videos, satellites data and many others. "Big Data" refers to a collection of massive volume of heterogeneous data that is being generated, often at high speed, from different sources. Traditional data management and analysis systems fall short of tools to analyze these data thus there is a need of innovative set of tools and frameworks to capture, process and manage these data within a tolerable elapsed time. Thus the concept of Big data is catching popularity faster than anything else in this technological era. Big Data demand cost-effective, fault tolerant, scalable and flexible and innovative forms of information processing for decision making[2]. This paper emphasis on the features, architectures, and functionalities of Big data, Hadoop, Map Reduce, HDFS.