Comparative Study of Apache Hadoop vs Spark

Authors

  • Varsha KR  Department of Computer Science and Engineering, RV College of Engineering Bangalore, Karnataka, India

Keywords:

Hadoop, Spark, Map-Reduce, HDFS

Abstract

The paper focuses on analyzing the differences and comparative study of two most talked about frameworks – Hadoop and Spark - both of which have increasing potential for the big data management. The analysis is carried out regarding components, design, data storage, recovery from failure among other features. Comparative analysis is made by executing certain algorithms on two platforms and comparing the execution time. Similarly, suitability of frameworks for different scenarios is discussed.

References

  1. Verma Ankush, Mansuri Ashik Hussain, Jain Neelesh, "Big Data Management Processing with Hadoop MapReduce and Spark Technology: A Comparison", 2016 Symposium on Colossal Data Analysis and Networking (CDAN).
  2. S Humbetov, "Data-intensive computing with map-reduce and hadoop", International Conference on Applcation of Information and Communication Technologies, pp. 1-5, 17-190ct. 2012.
  3. Polato Ivanilton, R Reginaldo, Goldman Alfredo, Kon Fabio, "A comprehensive view of Hadoop research-A systematic literature review", Journal of Network and Computer Applications, vol. 46, pp. 1-25, November 2014.
  4. online] Available: http://spark.apache.hadoop.org/.
  5. online] Available: http://spark.apache.org/docs/latest/mllib-Iinear-methods.html.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-10-30

Issue

Volume 3, Issue 7, September-October-2018

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Varsha KR, " Comparative Study of Apache Hadoop vs Spark" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 7, pp.390-394, September-October-2018.