Big Data Analytics and Its Benefits in Healthcare

Abstract

The main challenging task in real world is to collect huge amount of data from different sources in different format. Traditional database only helps in storing small amount of information. When the data become unstructured, it becomes difficult for the traditional database management system to extract knowledge out of it. For making an effective system, it becomes necessary to handle both structured and unstructured data. Here technology called big data solves this problem because it can extract the knowledge from structured as well as unstructured data. The purpose of big data is to collect the data that is gathered from different sources and then store this collected data in some common place. After then distributed File System is must for distributed storage and fault tolerance. Here Apache Hadoop is commonly being used these days. Another concept called Map reduce is a programming model that is most widely used in Hadoop for processing large amount of data quickly. In this paper big data are introduced in detail. Hadoop is used to process data in big data. There are many parts of Hadoop such as Hadoop common: these are the libraries of java and other modules which are included in Hadoop. Hadoop YARN which is used for cluster resource management and for job scheduling. Hadoop Distributed File System HDFS that help in providing greater amounts of access to application information and Hadoop MapReduc which is YARN based system which helps in processing parallel large data sets. The main purpose of the chapter is to use the function of big data in the fields of healthcare. Various examples as well as applications related to healthcare are discussed in this chapter. Various challenges related to big data analytics are discussed in this chapter.