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大数据承载的技术架构在近年来发展迅猛。数据规模大、产生速度快、来源多样等特性,使数据存储呈现复杂性。数据压缩等成本优化方案很大程度上降低了大数据的数据存储成本,也成为影响大数据平台普及和推广的关键技术路径。本文基于大数据的架构探讨了几种数据压缩方案,分析了不同方式数据压缩的应用场景,并对未来的数据处理方式提出了建议和展望。近年来,随着互联网的发展、移动互联网以及社交网络的兴起,全球每年产生的数据将以40%的速度增长,2009年到2020年之间将增长44倍(来自McKinsey Global Institute的数据)。海量数据的存储和基于海量数据的分析将会是带动未来生产力发展、创新、消费
The technology architecture that bears big data has grown rapidly in recent years. Large-scale data, produce fast, diverse sources and other characteristics of the data storage complexity. Data compression and other cost optimization programs greatly reduce the data storage cost of big data, but also become the key technology path that affects the popularization and promotion of big data platforms. This paper discusses several data compression schemes based on big data architecture, analyzes application scenarios of data compression in different ways, and puts forward suggestions and prospects for future data processing methods. In recent years, with the development of the Internet, the mobile Internet and the rise of social networks, the annual global data will grow by 40% and will increase 44 times between 2009 and 2020 (from the McKinsey Global Institute). Massive data storage and analysis based on massive data will be the driving force for future productivity growth, innovation, consumption