Learning the basics of a modeling technique is not the same as learning how to use and apply it. To develop a data model of an organization is to gain insights into its nature that do not come easily. Indeed, analysts are often expected to understand subtleties of an organization's structure that may have evaded people who have worked there for years.
Here's help for those analysts who have learned the basics of data modeling (or "entity/relationship modeling") but who need to obtain the insights required to prepare a good model of a real business.
Structures common to many types of business are analyzed in areas such as accounting, material requirements planning, process manufacturing, contracts, laboratories, and documents.
In each chapter, high-level data models are drawn from the following business areas:
The Enterprise and Its World
The Things of the Enterprise
Procedures and Activities
Contracts
Accounting
The Laboratory
Material Requirements Planning
Process Manufacturing
Documents
Lower-Level Conventions
About David C. Hay
David Hay was born in Grand Junction, Colorado, mid-way through the last century, when it was significant that his home town was some 250 miles from any city of any size. Back in those days, it mattered. His knowledge of the outside world was limited to magazines, movies, and the public library. (OK, he'd had some friends who'd been there, but he didn't believe a word of what they said.) It was all fiction. This valley was the whole world to him.
Then one beautiful September day, he took his first plane ride. Three hours later, he was by himself in the middle of Los Angeles International Airport at 5:00 on a Friday afternoon--trying to find his way to college.
Pretty much the rest of his life has been spent recovering from that afternoon.
The college was Claremont Men's College (now Claremont McKenna College) in the heart of the smoggy San Gabriel Valley. He remembers it as being pretty traumatic for him, but then this was Southern California in the late 1960s and life was traumatic--and exciting--for everyone. And this "outside world" business was pretty intriguing, too. So much so that when he graduated, he decided that the only logical thing to do was to move to New York City. Why not?
So, with no money, no job, no experience, and a degree in Philosophy, he set out to find his fortune in the big apple.
From there he discovered the rest of the world. Among other things, in 1973, he had a life-changing trip through Eastern Europe during the height of the Cold War. OK, that one wasn't quite so traumatic. He went back to Warsaw the following year to marry the single most wonderful woman he'd ever met.
He got his MBA from New York University the year after that.
In the late 1980s, he discovered data modeling. He took to it in a big way. But not the way most people did. Rather than viewing it as a vehicle for database design, he viewed it as a way to crack open the secrets of a company's semantics, and with that, its very nature. He discovered, among other things that if you model the underlying nature of a business, you have just modeled the underlying nature of pretty much any business.
From this experience came "Data Model Patterns: Conventions of Thought", a groundbreaking book describing a set of standard data models for standard business situations.
At about the same time, he created a consulting practice, Essential Strategies, Inc. (http://essentialstrategies.com), that offers data modeling services to a wide range of industries all over the world. He uses data modeling to support strategic planning, requirements analysis, analysis of semantics and business rules, and data warehouse design. His clients have included representatives of oil (both production and refining), pharmaceutical research, television and movies, banking, among others. In each case, he goes into the company knowing only what he's learned as a customer, and within a very short time (thanks to the model patterns) understands more about its underlying structure than many who work there.
In 2003, he wrote "Requirements Analysis: From Business Views to Architecture", his unique approach to that subject. This is a compendium of some thirty years' worth of analytical techniques, organized according to his version of John Zachman's "Framework for Enterprise Architecture".
Then, in 2006, he published "Data Model Patterns: A Metadata Map", the only book available that describes a complete schema of metadata--encompassing all aspects of both business and technical views. Moreover, it not only describes data from these various points of view, but also covers functions and processes, people and organizations, locations, timing, and motivation.
In 2011, as an update to the "...Conventions of Thought" book, he published "Enterprise Model Patterns: Describing the World". This does not invalidate anything in the first book, but it is more comprehensive, and it addresses the problem from multiple levels of abstraction. It also is the first business patterns book to use the UML notation.
Because that is a controversial thing to do, he also that year published "UML and Data Modeling: A Reconciliation", which is a discussion of the two approaches to modeling, and a handbook about how to use UML to create a business-oriented entity/relationship model.
He has been an active participant in DAMA International, various Oracle user groups, the Object Management Group, and the Business Rules Group. He has given presentations on various data and methodological subjects all over the world.
A library of his articles may be found at articles.essentialstrategies.com. Thanks to the World-wide Web, his writings are read by practitioners from all over the world.
Not bad for a kid from Grand Junction, Colorado, eh?
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说实话,我对数据建模这个领域一直有些敬而远之,总觉得它太偏向理论和晦涩的符号,直到我偶然接触到这本《Data Model Patterns》。这本书的叙事风格非常独特,它没有采用那种高高在上的说教口吻,反而更像是一本经验丰富的老工程师在茶余饭后分享他的“武功秘籍”。它巧妙地避开了过于深奥的数学推导,而是专注于“模式”(Patterns)的应用价值。书中对“事实表”和“维度表”的讲解,我以前在很多地方都看过,但从未像这次一样理解得如此透彻——作者用生动的比喻解释了它们在OLAP环境中的核心作用和设计考量,尤其是关于缓慢变化维度(SCD)的处理,提供了比标准教科书更具操作性的指导。我印象最深的是关于事件溯源(Event Sourcing)和领域驱动设计(DDD)在数据建模中的结合点,这部分内容展现了作者对现代系统架构的深刻洞察力。这本书的排版和插图也做得相当出色,那些流程图和实体关系图(ERD)清晰明了,帮助读者在大脑中构建起一个立体的模型视图。对于那些希望从“CRUD”操作者晋升为“系统架构师”的开发者来说,这本书绝对是一把开启思维大门的钥匙。它不仅教你“如何做”,更重要的是教你“为何要这么做”。
评分如果用一个词来形容我对《Data Model Patterns》的感受,那就是“扎实”。这不是一本轻飘飘的“速成”指南,它要求读者投入时间和精力去消化其中的精髓,但回报是巨大的。这本书的结构组织非常严谨,它首先确立了一套一致的术语体系,避免了不同建模流派之间的混淆。然后,它系统地引入了各种基础模式,如树形结构、快照模型等,并逐步将它们组合成更复杂的企业级解决方案。我发现,书中对性能优化的讨论不是孤立的,而是与数据模型的物理存储和逻辑结构紧密结合在一起,这体现了作者深厚的全栈视角。例如,关于如何设计索引友好的数据结构以支持高并发读取,书中给出的建议非常具体和具有操作性。我过去在设计报表数据库时,常常因为模型僵化而导致查询效率低下,这本书提供了处理动态报告需求的强大模型设计思路,比如利用“集合”和“聚合”的概念来预先组织数据。这本书更像是建筑师的设计蓝图,它关注的是长期稳定性和适应性,而不是短期的修补。对于希望将数据建模提升到工程艺术层面的专业人士来说,这绝对是一本值得反复研读的案头必备之作。
评分我是在一个跨职能的项目团队中接触到《Data Model Patterns》的,当时我们正面临着一个棘手的挑战:如何整合来自不同业务线、数据结构迥异的源系统。传统的关系型数据库设计方法在面对这种异构数据时显得力不从心。这本书的出现,简直是雪中送炭。它提供的那些成熟的数据模型范式,为我们提供了一个统一的语言和框架去讨论和设计新的数据架构。我最欣赏它在“时间性”数据处理上的深度剖析。在金融服务领域,时间戳的精确性和历史追溯能力至关重要。书中关于时间维度的设计准则,以及如何利用特定的模型来应对未来发生的变化(Forward-looking data),为我们解决了好几个悬而未决的设计难题。作者在书中强调了模型的可扩展性和可维护性,这在大型企业环境中是至关重要的指标。此外,书中对于非规范化(Denormalization)在特定场景下的合理性分析,也颠覆了我过去一味追求第三范式(3NF)的教条思维。这本书的深度足够让资深架构师受益匪浅,同时其清晰的结构也能引导新手快速上手,是一种罕见的兼顾深度和广度的佳作。
评分坦白说,我通常对技术书籍的期待值不高,很多都是炒冷饭或者堆砌理论。然而,《Data Model Patterns》成功地抓住了我的注意力,让我愿意放下手中的其他事务,一口气读完。它最迷人的地方在于其对“抽象”的驾驭能力。作者没有止步于简单的客户-订单模型,而是深入探讨了更复杂的领域,比如资源管理、权限控制和内容组织。书中的一些章节,比如关于“身份与访问管理”的数据模型设计,其精妙程度令人拍案叫绝,它清晰地展示了如何用数据结构来承载复杂的业务规则,而不是仅仅依赖于应用层的代码逻辑。阅读这本书的过程,对我来说更像是一次思维的迭代升级。它迫使我跳出自己熟悉的开发环境,去思考数据在整个企业生命周期中的角色和价值。我特别喜欢作者在每章末尾设置的“反思与挑战”部分,这些问题往往直指模型的薄弱环节,极大地激发了我的批判性思维。这本书的价值不在于提供“现成的代码”,而在于构建起一套强大的、可复用的思维工具箱,让你在面对任何新的数据挑战时,都能迅速找到最优雅的解决方案。
评分这本书简直是信息架构师的福音!我一直在寻找一本能深入浅出讲解数据建模核心思想的著作,而这本《Data Model Patterns》完美地填补了我的空白。它不仅仅是罗列了一堆技术术语,而是真正地从业务需求的本质出发,构建起坚实可靠的数据模型蓝图。书中对各种常见的业务场景——比如组织结构、交易流水、时间序列等——所对应的设计模式进行了详尽的剖析。我尤其欣赏作者在介绍每个模式时,都会穿插实际的案例分析,这让抽象的概念变得触手可及。例如,在讨论如何有效处理多对多关系时,书中给出的几种不同解决方案及其优劣对比,对我现有的项目架构起到了醍醐灌顶的作用。在阅读过程中,我感觉自己仿佛跟随一位经验丰富的大师在进行一对一的辅导,他对细节的把控和对潜在陷阱的预警都极为到位。这本书的逻辑性极强,从基础概念的铺垫到复杂模式的推演,层层递进,使得即便是初学者也能逐步建立起强大的数据建模思维。它不像某些教科书那样枯燥乏味,而是充满了实战的智慧和对行业最佳实践的深刻理解。看完之后,我立即将书中学到的知识应用到了我们部门正在进行的数据仓库重构中,效果立竿见影,极大地提高了数据的一致性和查询效率。强烈推荐给所有与数据打交道的人士。
评分如果这本书都不推荐,那真是说不过去了。
评分如果这本书都不推荐,那真是说不过去了。
评分如果这本书都不推荐,那真是说不过去了。
评分如果这本书都不推荐,那真是说不过去了。
评分如果这本书都不推荐,那真是说不过去了。
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