Analysis of social networking data using Map Reduce and Hadoop

The increasing use of internet technologies with the convergence of computing machines, from mainframe to cellular devices and to multimodal HCIS, has resulted into tremendous amount of data rich in volume and variety, since all data is not always required ,but only the relevant one suited for particular information need, some methods are required to supply user with application specific data .The technique to this huge amount of data and to extract value out of this volume and variety rich data are collectively called Big data. Over the recent years, there has been an emerging interest in big data for social media analysis. The three V’s of big data describe volume of data, variety types of data and velocity that defines the rate at which the data is processed by many social networking sites such as are considering big data. As big data is used when we speak usually in Peta-bytes and Exabyte’s of data due to this problem it is difficult for these social networking data to keep up the integration. In this paper we discuss about the big data, Mapreduce which plays an important role that supports big data to understand the problems of social networking site. Hadoop which is used with mapreduce to increase the performance of the big data by creating clusters on different nodes.