Analysis of big data security practices

P. Revathy, R. Mukesh
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引用次数: 7

Abstract

In modern world, huge amount of data is common across all businesses which aim to unlock new economy from these sources. Hadoop was developed to analyze large scale data repository in a parallel computing architecture. The main task in this process is to handle this “Big Data” by applying proper strategies. So, present industry is focusing on the methods in which this “Big Data” can be used for their business growth. There's no suspicion that the setup of Data Lake on hadoop can provide a new way of analytics and intuition analysis. Beyond experimentations and POCs, today Hadoop is considered more into production. As we are moving towards the stage where Hadoop is considered for real-time production scenarios and major chunk of the production data is normally sensitive, or subject to many control measures, it becomes high priority to consider the security aspects in hadoop before deciding on Hadoop installation for any enterprise. This paper evaluates various issues in Hadoop ecosystem and its popular distributions by top big data players in the market. It further intends to investigate and compare the current security features being provided in those big data distributions along with other open source big data security solutions to help in building secure big data environment.
大数据安全实践分析
在现代世界,大量的数据在所有旨在从这些资源中释放新经济的企业中都是常见的。开发Hadoop是为了在并行计算架构下分析大规模数据存储库。这个过程中的主要任务是通过适当的策略来处理这些“大数据”。因此,目前的行业正在关注如何利用这些“大数据”来促进业务增长。毫无疑问,在hadoop上建立数据湖可以提供一种新的分析和直觉分析方式。除了实验和poc之外,今天Hadoop被认为更多地用于生产。随着我们进入Hadoop被考虑用于实时生产场景的阶段,大部分生产数据通常是敏感的,或者受到许多控制措施的影响,在决定为任何企业安装Hadoop之前,考虑Hadoop的安全方面变得非常重要。本文评估了Hadoop生态系统中的各种问题以及市场上顶级大数据参与者的流行发行版。它进一步打算调查和比较这些大数据发行版中提供的当前安全特性以及其他开源大数据安全解决方案,以帮助构建安全的大数据环境。
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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