A Study of Hadoop and Mapping Approach Techniques on Big Data Strategies

Authors

  • Ms. Kamal Verma  Ph.D Research Scholar, Computer Science and Applications, Desh Bhagat University, Punjab India
  • Prof. (Dr.) R. K. Bathla  Professor, Computer Science and Applications, Desh Bhagat University, Punjab India

DOI:

https://doi.org//10.32628/IJSRST229656

Keywords:

Big Data, Cloud Services, Map Reduce Algorithm, Mapper, Reducer.

Abstract

Research is an art of scientific examination. The advance learner’s vocabulary of current English lays down the meaning of research as “A careful exploration and enquiry especially through search for new facts in any branch of knowledge. Bradman and Morry define research as “A standardize efforts to increase new knowledge”. Research is, thus an original contribution to existing stock of knowledge making for its advancement. It is detection of truth with the help of study, observation, comparison, and experiments. The technologies that give support to the entire process of cost-effectively storing and processing data, and utilize internet technologies in a scattered way have arisen in the past few years. NoSQL and Cloud computing are the renowned ones that improve the potential offered by Big Data Technologies. Map Reduce is a software manufacture introduced by Google to act upon parallel processing on large datasets supercilious that large dataset storage is distributed over a large number of machines. Each machine computes data stored locally, which in turn contributes to distribute and parallel processing. This paper focuses on the Big data and Cloud services using impact of Map Reduce Algorithm and very advantageous for the researchers and corporate sectors who are using Map Reducing System technology.

References

  1. Hadoop, “Poweredby Hadoop,”http://wiki.apache.org/hadoop/PoweredBy.
  2. Hadoop Tutorial,YahooInc., https://developer.yahoo.com/hadoop/tutorial/index.html
  3. Apache: Apache Hadoop, http://hadoop.apache.org
  4. Hadoop Distributed File System (HDFS), http://hortonworks.com/hadoop/hdfs/
  5. Jianqing Fan1, Fang Han and Han Liu, Challenges of Big Data analysis, National Science Review Advance Access published February, 2014.
  6. Haddop MapReduce- http://hadooptutorial.wikispaces.com/MapReduce
  7. Amazon Simple Storage Service (Amazon S3). http://aws.amazon.com/s3/
  8. Apache Hive, http://hive.apache.org/
  9. Jens Dittrich JorgeArnulfo Quian´eRuiz, Efficient Big Data Processing in Hadoop MapReduce.
  10. Changqing Ji, Yu Li, Wenming Qiu, Uchechukwu Awada, Keqiu Li, Big Data Processing in Cloud Computing Environments, 2012 International Symposium on Pervasive Systems, Algorithms and Networks.
  11. Y. Kim and K. Shim. TWITOBI: A recommendation system for twitter using probabilistic modeling. In ICDM, 2011.
  12. Y. Kim and K. Shim. Parallel top-k similarity join algorithms using Map Reduce. In ICDE, 2012.
  13. H. Li, Y. Wang, D. Zhang, M. Zhang, and E. Chang. PFP: Parallel FP-Growth for query recommendation. ACM Recommender Systems, 2008.
  14. A. Okcan and M. Riedewald. Processing theta-joins using MapReduce. In SIGMOD, 2011. [20] B. Panda, J. S. Herbach, S. Basu, and R. J. Bayardo. Planet: Massively parallel learning of tree ensembles with MapReduce. In VLDB, 2012.
  15. K. Zhai, J. L. Boyd-Graber, N. Asadi, and M. L. Alkhouja. Mr. LDA: A flexible large scale topic modeling package using variational inference in MapReduce. In WWW, 2012.
  16. “Big Data for Development: Challenges and Opportunities”, Global Pulse, May 2012 Yuri Demchenko The Big Data Architecture Framework (BDAF) Outcome of the Brainstorming Session at the University of Amsterdam 17 July 2013.

Downloads

Published

2022-12-30

Issue

Section

Research Articles

How to Cite

[1]
Ms. Kamal Verma, Prof. (Dr.) R. K. Bathla, " A Study of Hadoop and Mapping Approach Techniques on Big Data Strategies, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 9, Issue 6, pp.377-383, November-December-2022. Available at doi : https://doi.org/10.32628/IJSRST229656