Beyond Hadoop for e-commerce Big Data Analysis through Amazon

Ankush Verma, N. Sethi, Neelesh Jai
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Abstract

Analysis of big data is a challenging task as it involves large distributed file systems. The infrastructure require for analyzing big data is different from Amazon analysis technology and data mining on various types of data. Mapreduce is widely popular for analysis of big data. Mapreduce is working with mapping, sorting, shuffling and reducing using Master/Slave architecture. Similarly Amazon MapReduce programming model over large data set is introduced by Amazon, on the web especially used for ecommerce. In this paper Amazon EC2 cloud computing model used for central part of designed web and for collection and storing of large data Amazon uses S3. Amazon clusters is a group of servers which is working together to perform any type of tasks on distributed database on different servers in parallel. Amazon services are used in analysis of big data and to increase business efficiency
超越Hadoop,通过亚马逊进行电子商务大数据分析
大数据分析是一项具有挑战性的任务,因为它涉及大型分布式文件系统。分析大数据所需的基础设施不同于亚马逊的分析技术和对各种类型数据的数据挖掘。Mapreduce在大数据分析方面广受欢迎。Mapreduce使用主/从架构处理映射、排序、改组和约简。类似地,亚马逊在大型数据集上引入了Amazon MapReduce编程模型,在网络上特别用于电子商务。本文采用Amazon EC2云计算模型作为设计的web的中心部分,Amazon使用S3来收集和存储大数据。Amazon集群是一组服务器,它们一起工作,在不同的服务器上并行地执行分布式数据库上的任何类型的任务。亚马逊的服务用于大数据分析和提高业务效率
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