Big data analysis using Apache Hadoop

2013 IEEE 14th International Conference on Information Reuse & Integration (IRI) Pub Date : 2013-08-01 DOI:10.1109/IRI.2013.6642536

J. Nandimath, Ekata Banerjee, Ankur Patil, Pratima Kakade, S. Vaidya

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引用次数: 41

Abstract

The paradigm of processing huge datasets has been shifted from centralized architecture to distributed architecture. As the enterprises faced issues of gathering large chunks of data they found that the data cannot be processed using any of the existing centralized architecture solutions. Apart from time constraints, the enterprises faced issues of efficiency, performance and elevated infrastructure cost with the data processing in the centralized environment. With the help of distributed architecture these large organizations were able to overcome the problems of extracting relevant information from a huge data dump. One of the best open source tools used in the market to harness the distributed architecture in order to solve the data processing problems is Apache Hadoop. Using Apache Hadoop's various components such as data clusters, map-reduce algorithms and distributed processing, we will resolve various location-based complex data problems and provide the relevant information back into the system, thereby increasing the user experience.

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使用Apache Hadoop进行大数据分析

处理海量数据集的范式已经从集中式架构转向分布式架构。当企业面临收集大量数据的问题时，他们发现使用任何现有的集中式体系结构解决方案都无法处理这些数据。除了时间限制外，企业还面临集中式环境中数据处理的效率、性能和基础设施成本上升等问题。在分布式体系结构的帮助下，这些大型组织能够克服从庞大的数据转储中提取相关信息的问题。市场上用来利用分布式架构来解决数据处理问题的最好的开源工具之一是Apache Hadoop。使用Apache Hadoop的各种组件，如数据集群、map-reduce算法和分布式处理，我们将解决各种基于位置的复杂数据问题，并将相关信息提供回系统，从而增加用户体验。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)

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