大数据分析和大数据分析方法的比较

S. Malhotra, M. Doja, Bashir Alam, Mansaf Alam
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引用次数: 5

摘要

典型领域(例如互联网、金融公司、医疗保健、用户生成数据、供应链系统等)的最新技术进步导致这些领域的数据泛滥。数据爆发趋势为“大数据”这个热词赋予了深刻的意义。与传统数据相比,大数据显示出一些独特的特点,比如它通常是庞大的非结构化数据,而传统数据库无法处理这些数据。因此,在数据采集、数据传输、数据存储和大规模数据处理机制方面,需要新的系统设计。本文从多个方面阐述了大数据的定义。我们分析了大数据系统架构和大数据面临的各种挑战。本文还对当前流行的Hadoop框架、Hive、No SQL、New SQL、MapReduce和HBase进行了分析和比较,以解决大数据面临的最大挑战,即数据分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bigdata analysis and comparison of bigdata analytic approches
Recent technological advancements in typical domains (e.g. internet, financial companies, health care, user generated data, supply chain systems etc.) have directed to inundate of data from these domains. Data outburst trend gave the insight meaning to the buzz word ‘Bigdata’. If we compare with traditional data, Bigdata exhibits some unique characteristics like it is commonly enormous and unstructured type of data that cannot be handled using traditional databases. Hence new system designs are required for the following processes i.e. data collection, data transmission, storage, and large-scale data processing mechanisms. The definition of Bigdata has been presented from many aspects in this paper. We analyzed Bigdata system architecture and various challenges of Bigdata. The prevalent Hadoop framework, Hive, No SQL, New SQL, MapReduce and HBase for addressing the biggest challenge of Bigdata i.e Data Analytic has also been analyzed and compared.
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