In current scenario the rapid growth in the size of generated data is so huge and complex that traditional data processing application tools and platforms are inadequate to deal with it. Therefore, the big data require suitable analysis mechanisms for data processing and analysis in an efficient and effective manner. Consequently, developing and designing new scalable data mining techniques is very important and necessary mission for researchers and scientists in the last years. Scaling is the ability of the system to adapt to increased demands in terms of data processing. To support big data processing, different platforms incorporate scaling in different forms. We had tried to analyze these platforms on the basis of their performance in different environment.

Big data, Scalability, Hadoop

[1] Shao, H., L. Rao, Z. Wang, X. Liu, Z. Wang and K. Ren., “Optimal Load Balancing and Energy Cost Management for Internet Data Centers in Deregulated Electricity Markets”, IEEE Trans. Parall. Distr. Syst., Vol. 25, No. 10, pp. 2659–2669 , 2014.
[2] SWDS Li, J., Bao, Z. and Z. Li, “Modeling Demand Response Capability by Internet Data Centers Processing Batch Computing Jobs”, IEEE Trans. on Smart Grid, Vol. 6, No. 2, pp. 737–747, 2015.
[3] Liu, X., N. Iftikhar and X. Xie, “Survey of Real-Time Processing Systems for Big Data”, 18th Int. Database Engineering and Applications Symposium, New York, pp. 356–361, USA, 2014.
[4] Singh, K. and R. Kaur, “Hadoop: Addressing Challenges of Big Data”, 2014 IEEE Int. Advance Computing Conf., Navi Mumbai, pp. 686-689, India, 2014.
[5] Liu, X., N. Iftikhar and X. Xie, “Survey of Real-Time Processing Systems for Big Data”, 18th Int. Database Engineering and Applications Symposium, New York, pp. 356–361, USA, 2014
[6] Shao, H., L. Rao, Z. Wang, X. Liu, Z. Wang and K. Ren., “Optimal Load Balancing and Energy Cost Management for Internet Data Centers in Deregulated Electricity Markets”, IEEE Trans. Parall. Distr. Syst., Vol. 25, No. 10, pp. 2659–2669 , 2014.
[7] Singh, K. and R. Kaur, “Hadoop: Addressing Challenges of Big Data”, 2014 IEEE Int. Advance Computing Conf., Navi Mumbai, pp. 686-689, India, 2014.
[8] Sun, D., G. Fu, X. Liu and H. Zhang, “Optimizing Data Stream Graph for Big Data Stream Computing in Cloud Datacenter Environments”, Int. J. of Advancements in Computing Technology, Vol. 6, No. 5, pp. 53–65, 2014.
[9] K. Parimala, G. Rajkumar, A. Ruba, S. Vijayalakshmi, "Challenges and Opportunities with Big Data", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.5, pp.16- 20, 2017
[10] Sun, D., G. Zhang, S. Yang, Zheng W., S. U.Khan and K. Li, “Re-stream: Realtime and Energy-efficient Resource Scheduling in Big Data Stream Computing Environments”, Information Sciences, No. 319, pp. 92-112, 2015.
[11] Mantripatjit Kaur, Anjum Mohd Aslam, "Big Data Analytics on IOT: Challenges, Open Research Issues and Tools", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.81-85, 2018
[12] V.K. Gujare, P. Malviya, "Big Data Clustering Using Data Mining Technique", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.9-13, 2017.
[13] Shilpa Manjit Kaur, “BIG Data and Methodology- A review” ,International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 10, October 2013.