SECURITY AND CACHE MECHANISM IN HADOOP APPLICATION
Keywords:
Performance, algorithms, crucialAbstract
In earlier time traditional tools like SQL Databases, Files etc. were used to handle data and its issues. With increase in the volume of data, traditional tools struggled a lot to store, retrieve manipulate data and hence Hadoop and Big Data evolved. Security and Performance in any application is an issue which needs to be addressed with increasing expectation of immediate availability of data and information. Security poses a major challenge which can be addressed with the help with encryption and decryption mechanisms. The main objective of encryption is to safeguard the confidentiality of data stored on computer or transmitted via Internet or other media. In Modern era encryption algorithms play a crucial role in security assurance of Computer systems andcommunications across network as these algorithms can provide confidentiality, authenticity, data integrity and Non repudiation.Another aspect of today‟s modern application development is that developers have a wide variety of techniques and technologies available to improve application performance and end-user experience. One of the most widely used technologies is the cache mechanism. By using cache at the client side the applications cane greatly benefited by improving response times and reducing server I/O load. One of such examples is HTTP caching techniques which are always associated with the client side cache mechanisms.
References
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). [2] Kiran kumara Reddi & Dnvsl Indira “Different Technique to Transfer Big Data : survey” IEEE Transactions on 52(8) (Aug.2013) 2348 { 2355}
Jimmy Lin “MapReduce Is Good Enough?” The control project. IEEE Computer 32 (2013).
Umasri.M.L, Shyamalagowri.D ,Suresh Kumar.S “Mining Big Data:- Current status and forecast to the future” Volume 4, Issue 1, January 2014 ISSN: 2277 128X [5] Albert Bifet “Mining Big Data In Real Time” Informatica 37 (2013) 15–20 DEC 2012
Bernice Purcell “The emergence of “big data” technology and analytics” Journal of Technology Research 2013.
Sameer Agarwal†, Barzan MozafariX, Aurojit Panda†, Henry Milner†, Samuel MaddenX, Ion Stoica “BlinkDB: Queries with Bounded Errors and Bounded Response Times on Very Large Data” Copyright © 2013ì ACM 978-1-4503-1994 2/13/04
Yingyi Bu _ Bill Howe _ Magdalena Balazinska _ Michael D. Ernst “The HaLoop Approach to Large-Scale Iterative Data Analysis” VLDB 2010 paper “HaLoop: Efficient Iterative Data Processing on Large Clusters.
Shadi Ibrahim ⋆ _ Hai Jin _ Lu Lu “Handling Partitioning Skew in MapReduce using LEEN” ACM 51 (2008) 107–113
Kenn Slagter · Ching-Hsien Hsu “An improved partitioning mechanism for optimizing massive data analysis using MapReduce” Published online: 11 April 2013
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