This groundbreaking book is the first in the Kimball Toolkit series to be product-specific. Microsoft’s BI toolset has undergone significant changes in the SQL Server 2005 development cycle. SQL Server 2005 is the first viable, full-functioned data warehouse and business intelligence platform to be offered at a price that will make data warehousing and business intelligence available to a broad set of organizations. This book is meant to offer practical techniques to guide those organizations through the myriad of challenges to true success as measured by contribution to business value. Building a data warehousing and business intelligence system is a complex business and engineering effort. While there are significant technical challenges to overcome in successfully deploying a data warehouse, the authors find that the most common reason for data warehouse project failure is insufficient focus on the business users and business problems. In an effort to help people gain success, this book takes the proven Business Dimensional Lifecycle approach first described in best selling The Data Warehouse Lifecycle Toolkit and applies it to the Microsoft SQL Server 2005 tool set. Beginning with a thorough description of how to gather business requirements, the book then works through the details of creating the target dimensional model, setting up the data warehouse infrastructure, creating the relational atomic database, creating the analysis services databases, designing and building the standard report set, implementing security, dealing with metadata, managing ongoing maintenance and growing the DW/BI system. All of these steps tie back to the business requirements. Each chapter describes the practical steps in the context of the SQL Server 2005 platform. Intended Audience The target audience for this book is the IT department or service provider (consultant) who is: Planning a small to mid-range data warehouse project; Evaluating or planning to use Microsoft technologies as the primary or exclusive data warehouse server technology; Familiar with the general concepts of data warehousing and business intelligence. The book will be directed primarily at the project leader and the warehouse developers, although everyone involved with a data warehouse project will find the book useful. Some of the book’s content will be more technical than the typical project leader will need; other chapters and sections will focus on business issues that are interesting to a database administrator or programmer as guiding information. The book is focused on the mass market, where the volume of data in a single application or data mart is less than 500 GB of raw data. While the book does discuss issues around handling larger warehouses in the Microsoft environment, it is not exclusively, or even primarily, concerned with the unusual challenges of extremely large datasets. About the Authors JOY MUNDY has focused on data warehousing and business intelligence since the early 1990s, specializing in business requirements analysis, dimensional modeling, and business intelligence systems architecture. Joy co-founded InfoDynamics LLC, a data warehouse consulting firm, then joined Microsoft WebTV to develop closed-loop analytic applications and a packaged data warehouse. Before returning to consulting with the Kimball Group in 2004, Joy worked in Microsoft SQL Server product development, managing a team that developed the best practices for building business intelligence systems on the Microsoft platform. Joy began her career as a business analyst in banking and finance. She graduated from Tufts University with a BA in Economics, and from Stanford with an MS in Engineering Economic Systems. WARREN THORNTHWAITE has been building data warehousing and business intelligence systems since 1980. Warren worked at Metaphor for eight years, where he managed the consulting organization and implemented many major data warehouse systems. After Metaphor, Warren managed the enterprise-wide data warehouse development at Stanford University. He then co-founded InfoDynamics LLC, a data warehouse consulting firm, with his co-author, Joy Mundy. Warren joined up with WebTV to help build a world class, multi-terabyte customer focused data warehouse before returning to consulting with the Kimball Group. In addition to designing data warehouses for a range of industries, Warren speaks at major industry conferences and for leading vendors, and is a long-time instructor for Kimball University. Warren holds an MBA in Decision Sciences from the University of Pennsylvania's Wharton School, and a BA in Communications Studies from the University of Michigan. RALPH KIMBALL, PH.D., has been a leading visionary in the data warehouse industry since 1982 and is one of today's most internationally well-known authors, speakers, consultants, and teachers on data warehousing. He writes the "Data Warehouse Architect" column for Intelligent Enterprise (formerly DBMS) magazine.
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我曾向好几位刚入行的数据分析师推荐过这本书,但其中有一位反馈说,他觉得书里对“非结构化数据处理”和“高级时间序列分析”的部分写得不够深入。我想说,这是典型的“想一本书做所有事”的期待。这本书的定位非常明确:它是一本关于“微软数据仓库”的“工具箱”,核心是结构化数据(关系型数据库、OLAP立方体)的构建和维护。对于那些前沿的、需要大量机器学习模型预测、文本挖掘或图像分析的业务场景,这本书确实没有花笔墨去详述如何将这些“野蛮生长”的数据纳入到传统的星型模式中去进行高效查询。例如,如果你的业务核心是社交媒体情感分析,你需要处理大量的JSON或文本数据流,这本书提供的ETL方法可能过于偏重于批处理和预定义结构。它不会教你如何用Python和Pandas进行复杂的数据清洗和特征工程,也不会深入讲解如何使用Azure Data Factory (ADF) 来编排跨越多种数据源的复杂管道。因此,如果你的数据仓库项目注定要成为一个“混合体”,需要大量整合非关系型数据和实时流数据,那么这本书需要作为你的主要参考书之一,但你必须辅以更侧重于大数据平台和流处理技术的专业书籍来补全技能树的另一半。它精通于它所定义的领域,但在这个广阔的领域之外,它会适时地停下脚步。
评分坦白地说,这本书最大的“遗憾”或者说局限性,可能就是它对“云”的着墨相对较少。当我们现在谈论数据仓库时,AWS Redshift、Google BigQuery以及Azure Synapse Analytics这些现代云数据仓库解决方案几乎是绕不开的话题。而这本书,虽然名字带有“Microsoft”字样,但其核心关注点更偏向于本地部署的SQL Server和传统的SSIS/SSAS架构。它确实提到了Azure的一些概念,但更多的像是对新平台的简单介绍,而非深入的实践指南。对于一个希望立即构建一个基于Azure Databricks或Synapse的现代云数据湖屋的读者来说,这本书提供的“工具箱”可能需要进行大量的本地化改造和迁移。你仍然需要这本书提供的基础建模知识——因为底层逻辑是不变的——但构建和部署的脚本和技术栈需要完全重写以适应云环境的弹性伸缩和成本模型。因此,这本书更像是一部“坚实的基础乐高积木手册”,它教会你如何搭建一个完美的结构,但如果你想把这个结构搬到完全不同的“土地”(比如云端),你还需要额外学习一些关于土地改造和新材料使用的知识。它为经典架构提供了无懈可击的蓝图,但在快速迭代的云架构时代,这份蓝图可能需要“云计算”滤镜进行二次渲染。
评分说实话,如果你是一个已经身处数据领域多年,对Ralph Kimball和Bill Inmon的理论烂熟于心,并且每天都在和大型企业级数据湖、Spark集群打交道的资深架构师,那么这本书可能会让你觉得有些“朴素”。它聚焦于微软生态系统内部的解决方案,对于那些已经在云原生、实时数据流处理方面有深度投入的团队来说,其提供的解决方案的“前沿性”可能略显不足。它的优势在于“稳定”和“易于集成”,而非“颠覆性创新”。举个例子,书中详细阐述了如何利用SQL Server Reporting Services (SSRS) 进行固定的报表生成,这在如今很多企业依然是刚需,但对于习惯了Power BI的动态交互式仪表板的现代分析师来说,可能会觉得步骤繁琐。不过,换个角度看,对于那些预算有限、需要在一个相对封闭但功能强大的企业内部环境中快速搭建起一个可靠的数据中台的中小企业,这本书的价值就凸显出来了。它提供了一条清晰、低风险的路径,利用已经广泛部署的微软基础设施,实现数据资产的集中管理和分析。它的内容是扎实的、经过时间考验的,虽然不是最时髦的,但绝对是最可靠的基石之一。它像一本老派的瑞士军刀,虽然没有激光切割功能,但螺丝刀、钳子、开瓶器,样样都好用。
评分这本书简直是为数据仓库新手量身定做的指南,我当初拿到它的时候,手里还捧着好几本其他的数据仓库入门读物,但说实话,那些书要么过于理论化,要么就是罗列了一堆枯燥的术语,真正能上手操作的步骤少得可怜。然而,翻开这本《The Microsoft Data Warehouse Toolkit》,我立刻感受到了一种清晰、务实的风格。作者似乎非常理解初学者在面对微软技术栈时的那种既兴奋又茫然的心情。它没有一开始就扔出复杂的范式理论,而是从最基础的业务理解入手,一步步引导你构建一个能够实际运行的数据仓库模型。特别是书中对SQL Server Analysis Services (SSAS) 和SQL Server Integration Services (SSIS) 的讲解,简直是教科书级别的。他们没有仅仅停留在API调用层面,而是深入剖析了性能调优的关键点,比如如何合理地设计维度和事实表,以及在ETL过程中如何处理数据质量问题。我记得有一次,我为一个报表项目卡住了,数据量一大报表就崩溃,我按照书里介绍的一种基于星型模式的聚合策略进行优化,效果立竿见影。这本书的价值就在于,它把微软庞大工具集中的“工具”转化成了“可以解决实际问题的利器”,而不是堆砌在硬盘里吃灰的软件安装包。它提供的不仅仅是知识,更是一种解决问题的思维框架,让我从一个只会写简单查询的初级分析师,成长为一个能主导小型数据仓库项目的工程师。
评分这本书的排版和案例的代入感,是我个人非常欣赏的一点。很多技术书籍的作者似乎忘了,读者是人,不是机器。他们可以把最新的技术名词排列组合,但如果缺乏一个引人入胜的故事线或者一个贴近现实的业务场景,知识点就很难被吸收。这本书在这方面做得非常到位,它似乎是围绕着一个虚拟的零售企业展开,从最初的业务需求访谈,到数据源的梳理,再到最终报表的交付,全程都有详细的“剧本”。例如,在讲解如何处理“客户流失”这个业务指标时,它不是简单地给出一个复杂的T-SQL查询,而是先解释了为什么这个指标对零售商重要,然后才展示了如何在SSAS立方体中设计相应的度量值和层次结构。这种叙事方式极大地降低了理解复杂数据建模概念的门槛。我发现自己不再是机械地复制粘贴代码,而是真正开始思考“如果我是这个零售商的数据架构师,我会如何设计我的数据?” 此外,书中对于代码和截图的清晰度把握得非常好,大段的XML配置或者复杂的DAX表达式都有清晰的注释和分块展示,这在深夜调试代码时,简直是救命稻草,避免了因为一个遗漏的括号而导致的数小时的抓狂。
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