shubham upadhyay - Academia.edu (original) (raw)
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National University of Computer and Emerging Sciences
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Papers by shubham upadhyay
Data generated by the devices and the users in modern times is high in volume and variable in str... more Data generated by the devices and the users in modern times is high in volume and variable in structure. Collectively termed as Big Data, it is difficult to store and process using traditional processing tools. Traditional systems store data on physical servers or cloud resulting in higher cost and space complexity. In this paper, we provide a survey of various state-of-the-art research works done to handle the inefficient storage problem of Big Data. We have provided comparative literature to compare existing works to handle Big Data. As a solution to the problem encountered, we propose to split the Big Data into small chunks and provide each chunk to a different cluster for removing the redundant data and compressing it. Once every cluster has completed its task, the data chunks are combined back and stored on the cloud as compared to physical servers. This effectively reduces storage space and achieves parallel processing, thereby decreasing the processing time for very large dat...
Data generated by the devices and the users in modern times is high in volume and variable in str... more Data generated by the devices and the users in modern times is high in volume and variable in structure. Collectively termed as Big Data, it is difficult to store and process using traditional processing tools. Traditional systems store data on physical servers or cloud resulting in higher cost and space complexity. In this paper, we provide a survey of various state-of-the-art research works done to handle the inefficient storage problem of Big Data. We have provided comparative literature to compare existing works to handle Big Data. As a solution to the problem encountered, we propose to split the Big Data into small chunks and provide each chunk to a different cluster for removing the redundant data and compressing it. Once every cluster has completed its task, the data chunks are combined back and stored on the cloud as compared to physical servers. This effectively reduces storage space and achieves parallel processing, thereby decreasing the processing time for very large dat...