A Literature Survey on Big Data

This article explores the various tools and technologies currently being leveraged (like Hadoop, which is useful for developing applications that can perform absolute statistical analysis on vast quantities of data) and the issues faced when using them (heterogeneity and timeliness, security, incompleteness and scalability of the data are the biggest obstacles when analyzing big data). What are some additional areas where big data utilization can grow? What needs to improve? What other technologies do you envision being used in collaboration with big data in the future, and in what ways?

Abstract

There are many changes occurring in Cloud Computing, Big Data and Internet of things since past few years. Big Data is becoming main transformation for the enterprises and scientific society. Large and voluminous quantities of data are difficult to be handled by the traditional data analytics. Big data is the data that is very large in volume and also varied in mixture and is moving with great velocity. The major challenge is analyzing Big Data because it includes vast distributed file systems that should be error lenient, supple and accessible. Hadoop, Map Reduce, Apache Hive, No SQL and HPCC are the technologies used by big data application to handle the massive data. This paper provides a broad review of recent developments within the field of big data and its applications. Today, organizations are putting Big Data into practice in such diverse fields such as healthcare, smart cities, energy and finance.

Source: Monal Chaudhary, https://www.ijert.org/a-literature-survey-on-big-data
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