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Big data processing in the cloud - Challenges and platforms

机译:云中的大数据处理 - 挑战和平台

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Choosing the appropriate architecture and technologies for a big data project is a difficult task, which requires extensive knowledge in both the problem domain and in the big data landscape. The paper analyzes the main big data architectures and the most widely implemented technologies used for processing and persisting big data. Clouds provide for dynamic resource scaling, which makes them a natural fit for big data applications. Basic cloud computing service models are presented. Two architectures for processing big data are discussed, Lambda and Kappa architectures. Technologies for big data persistence are presented and analyzed. Stream processing as the most important and difficult to manage is outlined. The paper highlights main advantages of cloud and potential problems.
机译:为大数据项目选择适当的架构和技术是一项艰巨的任务,这在问题域和大数据景观中需要广泛的知识。本文分析了主要的大数据架构以及用于处理和持久的大数据的最广泛实现的技术。云提供动态资源缩放,这使其成为大数据应用的自然适合。提出了基本云计算服务模型。讨论了两个处理大数据的架构,Lambda和Kappa架构。提出和分析了大数据持久性的技术。流处理作为最重要且难以管理的流程。本文突出了云和潜在问题的主要优势。

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