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Aisha Siddiqa


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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.8 P.1040-1070


Big data storage technologies: a survey

Author(s):  Aisha Siddiqa, Ahmad Karim, Abdullah Gani

Affiliation(s):  Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia; more

Corresponding email(s):   aasiddiqa@gmail.com

Key Words:  Big data, Big data storage, NoSQL databases, Distributed databases, CAP theorem, Scalability, Consistency- partition resilience, Availability-partition resilience

Aisha Siddiqa, Ahmad Karim, Abdullah Gani. Big data storage technologies: a survey[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(8): 1040-1070.

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publisher="Zhejiang University Press & Springer",

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A1 - Aisha Siddiqa
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There is a great thrust in industry toward the development of more feasible and viable tools for storing fast-growing volume, velocity, and diversity of data, termed ‘big data’. The structural shift of the storage mechanism from traditional data management systems to NoSQL technology is due to the intention of fulfilling big data storage requirements. However, the available big data storage technologies are inefficient to provide consistent, scalable, and available solutions for continuously growing heterogeneous data. Storage is the preliminary process of big data analytics for real-world applications such as scientific experiments, healthcare, social networks, and e-business. So far, Amazon, Google, and Apache are some of the industry standards in providing big data storage solutions, yet the literature does not report an in-depth survey of storage technologies available for big data, investigating the performance and magnitude gains of these technologies. The primary objective of this paper is to conduct a comprehensive investigation of state-of-the-art storage technologies available for big data. A well-defined taxonomy of big data storage technologies is presented to assist data analysts and researchers in understanding and selecting a storage mechanism that better fits their needs. To evaluate the performance of different storage architectures, we compare and analyze the existing approaches using Brewer’s CAP theorem. The significance and applications of storage technologies and support to other categories are discussed. Several future research challenges are highlighted with the intention to expedite the deployment of a reliable and scalable storage system.




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