数据仓库,英文名称为Data Warehouse,可简写为DW或DWH。 数据仓库是决策支持系统和联机分析应用数据源的结构化数据环境。数据仓库研究和解决从数据库中获取信息的问题。其特征在于面向主题、集成性、稳定性和时变性。

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这本书将向你展示如何通过连接特定的Azure技术来组装数据仓库解决方案,这些技术可以满足你的需求并为你的业务带来价值。您将看到如何为数据池技术和SQL数据库使用批、事件和流实现一系列体系结构模式。您将了解如何管理元数据和自动化以加速仓库的开发,同时在每个级别上建立弹性。您还将知道如何提供下游分析解决方案,如Power BI和Azure分析服务,以增强数据驱动的决策能力,从而推动您的业务走向成功模式。

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Streaming data join is a critical process in the field of near-real-time data warehousing. For this purpose, an adaptive semi-stream join algorithm called CACHEJOIN (Cache Join) focusing non-uniform stream data is provided in the literature. However, this algorithm cannot exploit the memory and CPU resources optimally and consequently it leaves its service rate suboptimal due to sequential execution of both of its phases, called stream-probing (SP) phase and disk-probing (DP) phase. By integrating the advantages of CACHEJOIN, in this paper we present two modifications in it. First is called P-CACHEJOIN (Parallel Cache Join) that enables the parallel processing of two phases in CACHEJOIN. This increases number of joined stream records and therefore improves throughput considerably. Second is called OP-CACHEJOIN (Optimized Parallel Cache Join) that implements a parallel loading of stored data into memory while the DP phase is executing. We present the performance analysis of both of our approaches with existing CACHEJOIN empirically using synthetic skewed dataset.

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