SQL Versus NoSQL Movement with Big Data Analytics
SQL Versus NoSQL Movement with Big Data Analytics
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DOI：
10.5815/ijitcs.2016.12.07
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发表时间：
2016-12
期刊：
影响因子：
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通讯作者：
S. Venkatraman;Kiran Fahd;S. Kaspi;R. Venkatraman
中科院分区：
文献类型：
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作者：
S. Venkatraman;Kiran Fahd;S. Kaspi;R. Venkatraman
最近，数据管理领域发生了两次主要的革命，即大数据分析和NoSQL数据库。尽管它们的发展有着不同的目的，但它们的独立发展是相辅相成的，它们的融合将使企业在使用大量结构化和非结构化的复杂数据集进行实时决策时受益匪浅。虽然一方面出现了许多支持大数据分析的软件解决方案，但另一方面，许多NoSQL数据库包已经进入市场。然而，他们缺乏一个独立的基准和比较评价。本文的目的是提供对它们的上下文的理解，并深入研究比较已经发展的四种主要NoSQL数据模型的特征。传统SQL与NoSQL数据库用于大数据分析的性能比较表明，NoSQL数据库对于需要简单性，适应性，高性能分析和大型数据分布式可扩展性的业务情况来说是一个更好的选择。本文的结论是，NoSQL运动应该被用于大数据分析，并将与关系（SQL）数据库共存。
Two main revolutions in data management have occurred recently, namely Big Data analytics and NoSQL databases. Even though they have evolved with different purposes, their independent developments complement each other and their convergence would benefit businesses tremendously in making real-t ime decisions using volumes of complex data sets that could be both structured and unstructured. While on one hand many software solutions have emerged in supporting Big Data analytics, on the other, many NoSQL database packages have arrived in the market. However, they lack an independent benchmarking and comparat ive evaluation. The aim of this paper is to provide an understanding of their contexts and an in-depth study to compare the features of four main NoSQL data models that have evolved. The performance comparison of traditional SQL with NoSQL databases for Big Data analytics shows that NoSQL database poses to be a better option for business situations that require simplicity, adaptability, high performance analytics and distributed scalability of large data. This paper concludes that the NoSQL movement should be leveraged for Big Data analytics and would coexist with relational (SQL) databases.