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 ORCID:

Aisha Siddiqa

http://orcid.org/0000-0002-1016-758X

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Article info.

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

http://doi.org/10.1631/FITEE.1500441


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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Abstract: 
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.

大(dà)數據存儲技術綜述

概要:對于容量快速增長、日趨多元化的大(dà)數據,業界亟需開(kāi)發可行性更好的存儲工(gōng)具。爲滿足大(dà)數據存儲需求,存儲機制已經形成從傳統數據管理系統到NoSQL技術的結構化轉移。然而,目前可用的大(dà)數據存儲技術無法爲持續增長的異構數據提供一(yī)緻、可擴展和可用的解決方案。在科學實驗、醫療保健、社交網絡和電子商(shāng)務等實際應用中(zhōng),存儲是大(dà)數據分(fēn)析的第一(yī)步。截至目前,亞馬遜、谷歌和阿帕奇等公司形成了大(dà)數據存儲方案的行業标準,但尚未有關于大(dà)數據存儲技術性能和容量提升的深入調查和文獻報告。本文旨在對目前可用于大(dà)數據的最先進的存儲技術進行全面調查,提供了一(yī)個明确的大(dà)數據存儲技術分(fēn)類方法,以幫助數據分(fēn)析師和研究人員(yuán)了解和選擇更适合其需求的存儲機制。我(wǒ)們使用布魯爾的CAP定理比較和分(fēn)析了現有存儲方法,評估了不同存儲架構的性能,讨論了存儲技術的意義、應用及其對其他類别數據的支持。爲了加快部署可靠和可擴展的存儲系統,文中(zhōng)還突出了未來研究面臨的幾個挑戰。

關鍵詞:大(dà)數據;大(dà)數據存儲;NoSQL數據庫;分(fēn)布式數據庫;CAP定理;可擴展性;一(yī)緻性-分(fēn)區彈性;可用性-分(fēn)區彈性

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