Overview of Big Data Analytics System for Storing and Processing Huge Data
Abstract
Big data uses storage of huge data with some approaches and techniques to manage and process them. During the past few years the number of persons using internet, email and other internet based applications have been growing tremendously. Big Data is mainly characterized by Volume, Velocity and, Variety. The Big Data Architecture Framework (BDAF) is proposed to address all aspects of the Big Data Ecosystem and includes the following components: Big Data Infrastructure, Big Data Analytics, Data structures and models, Big Data Lifecycle Management, Big Data Security. The volume of data used is increasing exponentially. So, the need for storing, processing and protecting large volume of data has been becoming a great challenge in the modern hyper-connected world. Thousands of software professionals and others are doing their jobs with their internet connected laptops and mobile phones on the basis of work from home concept for development, implementation, testing and maintenance of various applications. These professionals and experts are sending and receiving lot of data to their clients, higher authorities and other officials on daily or weekly or other requirement basis. The traditional data management models are not efficient in Big data analytics. In this paper we try to give an overview of Big Data Analytics system for storing and processing huge data.
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Arushi Gupta, Asmita Sharma, Astha Sahu, Anjali Mukati and Ashlesha Panse,(2016),‘Study Of Pros And Cons In The Education System Using Big Data’,International Journal Of Engineering Sciences & ResearchTechnology.
Securing Big Data: Security Recommendations for Hadoop and NoSQL Environments www.securosis.com.
Yuri Demchenko, Canh Ngo, Peter Membrey., Architecture Framework and Components for the Big Data Ecosystem Draft Version 0.2
Miss Gurpreet Kaur Jangla and Mrs. Deepa.A.Amne, ‘Development of an Intrusion Detection System based on Big Data for Detecting Unknown Attacks’, ISSN (Online) 2278-1021 ISSN (Print) 2319 5940 International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 12, December 2015.
Harshawardhan S. Bhosale, Prof. Devendra P. Gadekar, (2014), ‘A Review Paper on Big Data and Hadoop’, International Journal of Scientific and Research Publications, Volume 4, Issue 10, ISSN 2250-3153.
Priya P. Sharma et al, (2014), ‘Securing Big Data Hadoop: A Review of Security Issues, Threats and Solution’, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (2), 2126-2131
Nishu Arora and Rajesh Kumar Bawa, (2014), ‘A Review on Cloud to Handle and Process Big Data’, International Journal of Innovations & Advancement in Computer Science IJIACS ISSN 2347 – 8616 Volume 3, Issue 5.
Big Data Analytics for Security Intelligence September 2013, CLOUD SECURITY ALLIANCE.
Seungwoo Jeon, Bonghee Hong, Joonho Kwon, Yoon-sik Kwak and Seok-il Song, (2013) ‘Redundant Data Removal Technique for Efficient Big Data Search Processing’, International Journal of Software Engineering and Its Applications Vol. 7, No. 4.
Prashant Kumar B and Khushboo Pandeya, (2013), ‘Big Data and Distributed Data Mining: An Example of Future Networks’, Volume 1, Issue 2 (2013) 36-39 ISSN 2347 - 3258 International Journal of Advance Research and Innovation.
AzzaAbouzeid, KamilBajdaPawlikowski, Daniel Abadi, Avi Silberschatz and Alexander Rasin, (2009),’ HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads’.
Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach Mike Burrows, Tushar Chandra, Andrew Fikes and Robert E. Gruber, (2006), ‘Bigtable: A Distributed Storage System for Structured Data’.
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