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Comparative Study of Big Data Frameworks

机译:大数据框架的比较研究

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摘要

We are really living in ever growing volume of data production. The huge amount of data in terabyte and petabytes are generating in real word and it is a challenging task to access, storage, analysis of all structured, unstructured and semi structured heterogeneous and complex data, also traditional tools is not suitable towards distributed and real-time processing. We need an efficient framework for processing such heterogeneous data and transform it into optimized meaningful information. There are many frameworks for distributed computing has been developed to perform huge amount of data processing. Hadoop Map Reduce is the extensively used framework because of its scalability, security, latency and efficiency, and reliability. The intension of this paper is to relative study of common framework such as Hadoop, Spark, Flink, Samza and Storm.
机译:我们真的生活在不断增长的数据生产量。 Terabyte和Petabytes中的大量数据是在真正的单词中产生的,并且访问,存储,分析所有结构化,非结构化和半结构化异构和复杂数据是一个具有挑战性的任务,而且传统工具也不适合分布和实际时间处理。我们需要一个有效的框架来处理此类异构数据并将其转换为优化的有意义信息。已经开发出许多用于分布式计算的框架来执行大量的数据处理。 Hadoop地图减少了广泛使用的框架,因为其可扩展性,安全性,延迟和效率和可靠性。本文的内容是对普通框架的相对研究,如Hadoop,Spark,Flink,Samza和Storm。

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