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Bigdata analysis and comparison of bigdata analytic approches

机译:大数据分析与大数据分析方法的比较

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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.
机译:典型领域(例如,互联网,金融公司,医疗保健,用户生成的数据,供应链系统等)中的最新技术进步已导致来自这些领域的数据泛滥。数据爆发趋势使流行语“ Bigdata”具有洞察力。如果我们将其与传统数据进行比较,则Bigdata具有一些独特的特征,例如它通常是巨大的,非结构化的数据类型,无法使用传统数据库进行处理。因此,以下过程即数据收集,数据传输,存储和大规模数据处理机制需要新的系统设计。本文从多个方面介绍了Bigdata的定义。我们分析了Bigdata系统架构和Bigdata的各种挑战。还分析了比较了流行的Hadoop框架,Hive,No SQL,New SQL,MapReduce和HBase,以应对Bigdata的最大挑战。

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