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Big Data Performance Evaluation in Hadoop Eco System

S. Srilakshmi1 , CH. Mallikarjuna Rao2

Section:Review Paper, Product Type: Journal Paper
Volume-7 , Issue-5 , Page no. 1131-1135, May-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i5.11311135

Online published on May 31, 2019

Copyright © S. Srilakshmi, CH. Mallikarjuna Rao . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: S. Srilakshmi, CH. Mallikarjuna Rao, “Big Data Performance Evaluation in Hadoop Eco System,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1131-1135, 2019.

MLA Style Citation: S. Srilakshmi, CH. Mallikarjuna Rao "Big Data Performance Evaluation in Hadoop Eco System." International Journal of Computer Sciences and Engineering 7.5 (2019): 1131-1135.

APA Style Citation: S. Srilakshmi, CH. Mallikarjuna Rao, (2019). Big Data Performance Evaluation in Hadoop Eco System. International Journal of Computer Sciences and Engineering, 7(5), 1131-1135.

BibTex Style Citation:
@article{Srilakshmi_2019,
author = {S. Srilakshmi, CH. Mallikarjuna Rao},
title = {Big Data Performance Evaluation in Hadoop Eco System},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {7},
Issue = {5},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {1131-1135},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4374},
doi = {https://doi.org/10.26438/ijcse/v7i5.11311135}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i5.11311135}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4374
TI - Big Data Performance Evaluation in Hadoop Eco System
T2 - International Journal of Computer Sciences and Engineering
AU - S. Srilakshmi, CH. Mallikarjuna Rao
PY - 2019
DA - 2019/05/31
PB - IJCSE, Indore, INDIA
SP - 1131-1135
IS - 5
VL - 7
SN - 2347-2693
ER -

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Abstract

In an everyday life, the limit of information expanded hugely with time. The development of information which will be unmanageable in person to person communication destinations like Facebook, Twitter. In the previous two years the information stream can increment in zettabyte. To deal with huge information there are number of uses has been produced. Nonetheless, investigating huge information is an exceptionally difficult errand today. Enormous Data alludes to activities and advances that include information that is excessively assorted, fast changing or immense for traditional innovations, aptitudes and framework to address productively. The present foundation to deal with enormous information isn`t effective as a result of information limit. The handling of huge information issue can be illuminated by utilizing MapReduce strategy. The effective usage of MapReduce show requires parallel handling and arranged joined capacity. Hadoop and Hadoop Distributed File System (HDFS) by apache are normally utilized for putting away and overseeing huge information. In this exploration work we recommend diverse strategies for taking into account the issues close by through MapReduce.

Key-Words / Index Term

MapReduce; Big Data; Zettabyte; Hadoop; Hadoop Distributed File System

References

[1] Jianqing Fan1, Fang Han and Han Liu, Challenges of Big Data analysis, National Science Review Advance Access published February, 2014.
[2] Lee, D., Kim J-S. & Maeng, S. (2013) A Large-scale incremental processing with MapReduce. FutureGeneration Computer System, 36, pp 66-79.
[3] VinayakBorkar, Michael J. Carey, Chen Li, Inside “Big Data Management”: Ogres, Onions, or Parfaits?, EDBT/ICDT 2012 Joint Conference Berlin, Germany,2012 ACM 2012, pp 3-14.
[4] Jiang, D., Tung, A. & Chen, G. (2011) MAP-JOIN-REDUCE: Toward Scalable and Efficient Data Analysis on Large Clusters. IEEE Transactions on Knowledge and Data Engineering, 23(9), 1299-1311.
[5] Gu, R., Yang, X., Yan, J., Sun, Y., Wang, B., Yuan, C. & Huang, Y. (2014) SHadoop: Improving MapReduce Performance by Optimizing Job Execution Mechanism in Hadoop Clusters. Journal of Parallel and Distributed Computing, 74(3), 2166-2179.
[6] Afrati, F.N. & Ullman, J.D. (2011) Optimizing Multiway Joins in a Map-Reduce Environment. IEEE Transactions on Knowledge and Data Engineering, 23(9), 1282-1298.
[7] Y. Yuan, Y. Wu, X. Feng, J. Li, G. Yang, W. Zheng, “VDB-MR: MapReduce-based distributed data integration using virtual database”, Future Generation Computer Systems 26 (2010) 1418–1425.
[8] Hadoop: open source implementation of MapReduce,http://hadoop.apache.org/mapreduce/>.
[9] R. Baraglia, G. D. F. Morales, and C. Lucchese. Document similarity self-join with MapReduce. In ICDM, 2010.
[10] Y. He, H. Tan, W. Luo, H. Mao, D. Ma, S. Feng, and J. Fan. Mr-dbscan: An efficient parallel density-based clustering algorithm using MapReduce. In ICPADS, 2011.
[11] Hadoop,“PoweredbyHadoop,”http://wiki.apache.org/hadoop/PoweredBy.
[12] Bakshi, K. (2012) Considerations for Big Data: Architecture and Approach. IEEE AerospaceConference,(pp.1-7).Big Sky,USA.
[13] Hadoop Tutorial, YahooInc., https://developer.yahoo.com/hadoop /tutorial/index.html
[14] Apache: Apache Hadoop, http://hadoop.apache.org.
[15] Hadoop Distributed File System (HDFS),http://hortonworks.com/hadoop/hdfs/.