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Received:April 04, 2011 Revised:July 21, 2011
Received:April 04, 2011 Revised:July 21, 2011
Abstract:In many areas such as science, simulation, Internet, and e-commerce, the volume of data to be analyzed grows rapidly. Parallel techniques which could be expanded cost-effectively should be invented to deal with the big data. Relational data management technique has gone through a history of nearly 40 years. Now it encounters the tough obstacle of scalability, which relational techniques can not handle large data easily. In the mean time, none relational techniques, such as MapReduce as a typical representation, emerge as a new force, and expand their application from Web search to territories that used to be occupied by relational database systems. They confront relational technique with high availability, high scalability and massive parallel processing capability. Relational technique community, after losing the big deal of Web search, begins to learn from MapReduce. MapReduce also borrows valuable ideas from relational technique community to improve performance. Relational technique and MapReduce compete with each other, and learn from each other; new data analysis platform and new data analysis eco-system are emerging. Finally the two camps of techniques will find their right places in the new eco-system of big data analysis.
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QIN Xiong-Pai,WANG Hui-Ju,DU Xiao-Yong,WANG Shan.Big Data Analysis—Competition and Symbiosis of RDBMS and MapReduce.Journal of Software,2012,23(1):32-45
QIN Xiong-Pai,WANG Hui-Ju,DU Xiao-Yong,WANG Shan.Big Data Analysis—Competition and Symbiosis of RDBMS and MapReduce.Journal of Software,2012,23(1):32-45