Emergence of Big Data on Cloud Computing (An Insight to Current and Future)

Amar Nath Singh¹ and Rakesh Kumar Rath^1,

¹GIET, Baniatangi, Bhubaneswar

Cite this paper:
Amar Nath Singh and Rakesh Kumar Rath. Emergence of Big Data on Cloud Computing (An Insight to Current and Future). Journal of Computer Sciences and Applications. 2015; 3(6):162-165. doi: 10.12691/jcsa-3-6-10

Abstract

In Database the prime objective of database is to arrange the data in a well designed manner so that the data should be stored and retrieved effectively as and when required. But in traditional database like DBMS the major fault was scalability. So in this database management systems (DBMS) the following problems are arise always such as, 1. Update intensive application workloads, and 2. Decision support systems for descriptive and deep analytics. These are a critical part of the cloud infrastructure and play an important role in ensuring the smooth transition of applications from the traditional enterprise infrastructures to next generation cloud infrastructures. So these scalability natures of the data base cause a lot of problem for data integration when the amounts of data were huge. This paper presents an organized picture of the challenges faced by application developers and DBMS designers in developing and deploying internet scale applications. We have designed this paper on the basis of the following aspects, such as: (i) For supporting update heavy applications, and (ii) For ad-hoc analytics and decision support. Here we also focus on providing an in-depth analysis of systems for supporting update intensive web-applications which are mostly now a day’s used in the companies and provide a survey of the state-of-the art in this domain. We have tried to crystallize the design aspect of choices made by considering some large scale database management systems, analyze the application demands and access patterns, and enumerate the desiderata for a cloud-bound DBMS.

Keywords:
cloud big data crystallization data base scalable data

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References:

[1]	A. Abouzeid, K. B. Pawlikowski, D. J. Abadi, A. Rasin, and A. Silberschatz. HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads. PVLDB, 2(1): 922-933, 2009.
[2]	D. Agrawal, S. Das, and A. E. Abbadi. Big data and cloud computing: New wine or just new bottles? PVLDB, 3(2):1647-1648, 2010.
[3]	D. Agrawal, A. El Abbadi, S. Antony, and S. Das. Data Management Challenges in Cloud Computing Infrastructures. In DNIS, pages 1-10, 2010.
[4]	P. Agrawal, A. Silberstein, B. F. Cooper, U. Srivastava, and R. Ramakrishnan. Asynchronous view maintenance for vlsd databases. In SIGMOD Conference, pages 179-192, 2009.
[5]	S. Aulbach, D. Jacobs, A. Kemper, and M. Seibold. A comparison of flexible schemas for software as a service. In SIGMOD, pages 881-888, 2009.
[6]	P. Bernstein, C. Rein, and S. Das. Hyder – A Transactional Record Manager for Shared Flash. In CIDR, 2011.
[7]	M. Brantner, D. Florescu, D. Graf, D. Kossmann, and T. Kraska. Building a database on S3. In SIGMOD, pages 251-264, 2008.
[8]	F. Chang, J. Dean, S. Ghemawat, W. C. Hsieh, D. A. Wallach, M. Burrows, T. Chandra, A. Fikes, and R. E. Gruber. Bigtable: A Distributed Storage System for Structured Data. In OSDI, pages 205-218, 2006.
[9]	J. Cohen, B. Dolan, M. Dunlap, J. M. Hellerstein, and C. Welton. Mad skills: New analysis practices for big data. PVLDB, 2(2):1481-1492, 2009.
[10]	B. F. Cooper, R. Ramakrishnan, U. Srivastava, A. Silberstein, P. Bohannon, H.-A. Jacobsen, N. Puz, D. Weaver, and R. Yerneni. PNUTS: Yahoo!’s hosted data serving platform. Proc. VLDB Endow., 1(2):1277-1288, 2008.
[11]	C. Curino, E. Jones, Y. Zhang, E. Wu, and S. Madden. Relational Cloud: The Case for a Database Service. Technical Report 2010-14, CSAIL, MIT, 2010. http://hdl.handle.net/1721.1/52606.
[12]	S. Das, S. Agarwal, D. Agrawal, and A. El Abbadi. ElasTraS: An Elastic, Scalable, and Self Managing Transactional Database for the Cloud. Technical Report 2010-04, CS, UCSB, 2010.
[13]	S. Das, D. Agrawal, and A. El Abbadi. ElasTraS: An Elastic Transactional Data Store in the Cloud. In USENIX HotCloud, 2009.
[14]	S. Das, D. Agrawal, and A. El Abbadi. G-Store: A Scalable Data Store for Transactional Multi key Access in the Cloud. In ACM SOCC, 2010.