Survey On Big Data Analytcis using Hadoop ETL

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2017 by IJCTT Journal
Volume-48 Number-1
Year of Publication : 2017
Authors : M.Saranya, A.Prema
DOI :  10.14445/22312803/IJCTT-V48P105

MLA

M.Saranya, A.Prema "Survey On Big Data Analytcis using Hadoop ETL". International Journal of Computer Trends and Technology (IJCTT) V48(1):19-23, June 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
The term big data refers to data sets whose volume, variability and speed of velocity make them difficult to capture, manage, procedure or analyzed. To examine this huge amount of data Hadoop is able to be used. Hadoop is an open source software project that enables the spread giving out of large data sets across a cluster of creation servers.ETL tools extract important information from various data sources, various transformation’s of data are established out transformation phase and then load into the big data. HDFS ( Hadoop Distributed File System), is a spread file system design to hold the very huge of data (petabytes or even zettabytes), and there high throughput admission to this information. Map Reduce method has been calculated in this paper which is required for implement Big Data Analysis using HDFS. In this paper the related topics of Big Data Analytics, and Hadoop, ETL, Map Reduce are reviewed.

References
[1] Varsha B.Bobad , “International Research Journal of Engineering and Technology (IRJET)”, Volume: 03 Issue: 01
[2] Inmon, William "Data Mart Does Not Equal Data Warehouse". DM Review.com. (2000-07-18).
[3] Jeffrey R. Bocarsly, “The Data Warehouse Toolkit.” Complex ETL Testing-A Strategic Approach
[4] R. Kimball and M. Ross. WileyPublishing, Inc., 2002.
[5] “Survey of Recent Research Progress and Issues in Big Data” www.cse.wustl.edu/~jain/cse570-13/ftp/bigdata2/index.html 1/13
[6] Dhole Poonam B, Gunjal Baisa L, “Survey Paper on Traditional Hadoop and Pipelined Map Reduce” International Journal of Computational Engineering Research||Vol, 03 Issue, 12
[7] Varsha B.Bobade” Survey Paper on Big Data and Hadoop” International Research Journal of Engineering and Technology
[8] V. Bhanumurthy*, G Behera “Deliverable from space Data sets for Disaster Management-present and true trends”
[9] Ms. Vibhavari Chavan, Prof. Rajesh. N. Phursule, ?”Survey Paper on Big Data”? International Journal of Computer Science and Information Technologies, Vol. 5 (6), 2014.
[10] Amogh Pramod Kulkarni, Mahesh Khandewal, ?”Survey on Hadoop and Introduction to YARN”?, International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 5, May 2014)
[11] Tekiner F. and Keane J.A., Systems, Man and Cybernetics (SMC), ?”Big Data Framework?” 2013 IEEE International Conference on 13–16 Oct. 2013, 1494–1499
[12] Inmon, William (2000-07-18). "Data Mart Does Not Equal Data Warehouse". DMReview.com
[13] Katarina Grolinger, Miriam A.M. Capretz.” Knowledge as a Service Framework for Disaster Data Management”
[14] V. Hristidis, S. Chen, T. Li, S. Luis, and Y. Deng, "Survey of Data Management and Analysis in Disaster Situations," Journal of Systems and Software, vol. 83, no. 10, pp. 1701-1714, 2010.
[15] Song .Y, Davis Karen C,” Analytics over large scale Multidimensional Data: The Big Data Revolution, Communications of ACM,” 2011
[16] Merinela Mircea,” Business Intelligence--Solution for Business Development”, Intech Publisher, 2012
[17] Kuldeep deshpande, and dr. Bhimappa desai,”limitations of dataware house platforms and Assessment of hadoop as an alternative,” Volume 5, Issue 2, pp. 51-58, IJITMIS ,2014
[18] Yongqiang He et al RCFile: “A Fast and Space-efficient Data Placement Structure in MapReduce-based Warehouse Systems,” ICDE, 2011
[19] Vibhavari Chavan et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (6) , 2014, 7932-7939
[20] Franklin, M.,Halevy, A., Maier, D., 2005. From databases to data spaces:”A new Abstraction For Information Management. ACM SIGMOD Record” 34 (4), 27–33.
[21] Saleem, K., Luis, S., Deng, Y., Chen, S.-C., Hristidis, V., Li, T., 2008. "Towards a business Continuity information network for rapid disaster recovery.” In: Proceedings of the 9th Annual International Conference on Digital Government Research, Montreal, Canada, May 18–21, pp. 107–116
[22] Senthi Vadivel Bhupatthi Rav” Disaster Management: A Global Issue” International journal of civil and structural engineering Volume 1, No 1, 2010
[23] Sagiroglu, S.Sinanc, D.,?Big Data: A Review?,2013, 20-24.
[24] Tekiner F. and Keane J.A., Systems, Man and Cybernetics (SMC), ?”Big Data Framework”? 2013 IEEE International Conference on 13–16 Oct. 2013, 1494–1499
[25] S.Vikram Phaneendra & E.Madhusudhan Reddy “Big Data- solutions for RDBMS problems- A survey” In 12th IEEE/IFIP Network Operations & Management Symposium (NOMS 2010) (Osaka, Japan, Apr 19{23 2013).

Keywords
Big Data, Hadoop, ETL, Map Reduce, HDFS.