Big Data Analytics and Its Benefits in Healthcare

  • Yogesh KumarEmail author
  • Kanika Sood
  • Surabhi Kaul
  • Richa Vasuja
Part of the Studies in Big Data book series (SBD, volume 66)


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.


HDFS Big data Hadoop Healthcare Map reduce 


  1. 1.
    Fatt: The usefulness and challenges of big data in healthcare abstract the usefulness and challenges of big data in healthcare data modeling, mobile big data analytics in healthcare. iMedPub J. (2018)Google Scholar
  2. 2.
    Russom: Big data analytics. TDWI best practices report, fourth quarter 19(4), 1–34 (2011)Google Scholar
  3. 3.
    Nambiar: A Look at Challenges and Opportunities of Big Data Analytics in Healthcare, pp. 17–22 (2013)Google Scholar
  4. 4.
    Shvachko, et al.: The hadoop distributed file system. In: 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), pp. 1–10 (2010)Google Scholar
  5. 5.
    Zikopoulos, et al.: Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. McGraw-Hill Osborne Media (2011)Google Scholar
  6. 6.
    Summary, et al.: Big Data is the Future of Healthcare, pp. 1–7 (2012)Google Scholar
  7. 7.
    Raghupathi, et al.: Big data analytics in healthcare: promise and potential. Health Inform. Sci. Syst. 2(1), 1–3 (2014)CrossRefGoogle Scholar
  8. 8.
    Bates, et al.: Downloaded from by Health Affairs on 15 Sept 2014
  9. 9.
    Dates, et al.: Call for Book Chapters Big Data Analytics in Healthcare Springer Series : Studies in Big Data (Link) Purpose and Scope : Editors of Book, pp. 3–4 (2019)Google Scholar
  10. 10.
    Belle, et al.: Big Data Analytics in Healthcare, pp. 12–17 (2015)Google Scholar
  11. 11.
    Archena, et al.: A survey of big data analytics in healthcare and government. Procedia Comput. Sci. 50, 408–413 (2015)CrossRefGoogle Scholar
  12. 12.
    Belle, et al.: Big data analytics in healthcare. BioMed Res. Int. (2015)Google Scholar
  13. 13.
    Sun, et al.: Big data analytics for healthcare. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1525–1525. ACM (2013)Google Scholar
  14. 14.
    Sarwar, et al.: A survey of big data analytics in healthcare. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 8(6), 355–359 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Yogesh Kumar
    • 1
    Email author
  • Kanika Sood
    • 2
  • Surabhi Kaul
    • 2
  • Richa Vasuja
    • 3
  1. 1.CSEChandigarh Engineering College, LandranMohaliIndia
  2. 2.CSEIET BhaddalDistrict RoparIndia
  3. 3.CSEChandigarh University GharuanMohaliIndia

Personalised recommendations