两层数据仓库设计的好处包括。
领域专家，为数据仓库设计提供业务领域知识。
Domain experts, who provide business domain knowledge for the data warehouse design.
在构建比较时匹配粒度级别是数据仓库设计中的关键概念。
Matching up grain levels when building comparisons is a critical concept in designing a data warehouse.
数据仓库架构师，是数据发现和数据仓库设计中的关键人物。
A data warehouse architect, who is the key person in data discovery and data warehouse design.
然而，请记住高级数据仓库设计应该包括所有的业务主题领域。
However, keep in mind that high-level data warehouse design should include all business subject areas.
然而，正式的数据仓库设计应该从数据仓库的架构开始。
However, the formal data warehouse design should begin with data warehouse architecture.
稍后您将了解更多关于操作型数据和数据仓库设计的信息。
You'll learn more about the operational data and the data warehouse design shortly.
学习这些场景时，您将更多地了解运营数据和数据仓库设计。
You'll learn more about the operational data and the data warehouse design as you step through each scenario.
重点讨论并给出了一种规范化的数据仓库设计和粒度模型设计方法。
The normalized methods for data warehouse and data granularity model designing are given and discussed. Granularity model designing is very important in data warehousing.
对于数据仓库设计人员而言，理解设计过程中包含的12个职责是很重要的。
For data warehouse designers, it is important to understand the 12 responsibilities involved in such a designing process.
当使用数据模型捕获业务需求时，您就已经完成了数据仓库设计中的部分工作。
When you use data models to capture business requirements, you are already doing some part of the data warehouse design work.
数据仓库设计和数据建模是计算机科学和IT结合的产物，众所周知、意义重大。
Data warehousing design and data modeling is a well-known, significant blend of computer science and IT.
自底向上的实现包含数据仓库的计划和设计，无需等待安置好更大业务范围的数据仓库设计。
A bottom-up implementation involves the planning and design of a data warehouse without waiting for a more business-wide data warehouse design to be put in place.
实现方法的选择是数据仓库设计中的重要部分；该决策需要在数据仓库项目的早期阶段做出。
The selection of an implementation approach is a very important part of data warehouse design; the decision needs to be made at an early stage of the data warehouse project.
建立数据模型是构造数据仓库的重要步骤之一，多维数据模型是数据仓库设计中广泛采用的概念模型。
Constructing data model is one of important steps for building data Warehouse. Multidimensional model is a conceptual model widely used in data Warehouse designing.
完成了数据库构成、建设原则、逻辑设计、物理结构设计、原数据设计、数据仓库设计、安全维护等。
The database composing, the construction principle, the logic design, physical structure design, the original data design, the data warehouse design and the security maintenance were completed.
本文首先就数据仓库与传统关系数据库作了一个简单比较，然后介绍了一种适合于数据仓库设计的方法。
This paper first compares data warehouse with traditional relation database, then introduces a method for designing data warehouse.
这并不意味着不会开发更大业务范围的数据仓库设计；随着初始数据仓库实现的扩展，将逐渐增加对它的构建。
This does not mean that a more business-wide data warehouse design will not be developed; it will be built incrementally as initial data warehouse implementations expand.
Informix 11.7也有许多令人兴奋的特性，用于数据仓库设计和开发，我们仍然使用几个非常复杂的查询进行测试。
Informix 11.7 also has a number of exciting new features for data warehouse design and development that we are still testing, using several very complex queries.
最后，将本文研究的面向对象的数据仓库设计方法应用于某商业银行稽核系统，在系统建模和业务功能实现上取得了显著成效。
In the end, we applied this design method suggested by this article to the bank audit system, and received good effect on system modeling and business function.
继续本系列中的下一篇文章，其中将介绍数据仓库的设计和实现阶段。
Stay tuned for the next article in this series, which covers the data warehouse design and implementation stages.
差异分析十分重要，因为它是您数据仓库项目建议和设计的基础。
The gap analysis is important, because it is the basis for your data warehouse project proposal and design.
事实上，我们已经看到，IWA取代了我在数据仓库中设计的汇总表中的许多需求。
In fact, I see IWA replacing the need for many of the summary tables I design in a data warehouse.
然而，有价值的数据仓库的两个关键就是其灵活性以及处理设计时未知的查询的能力。
However, two keys to a valuable data warehouse are its flexibility and its ability to handle queries that are unknown at design time.
如果它们太具体，就可以能影响数据仓库的设计方式，可能排除看似无关但可能对于所执行的分析十分关键的因素。
If they are too specific, they may influence the way the data warehouse is designed, to the point of excluding factors that seem irrelevant but may be key to the analysis being conducted.
设计维度数据仓库是一个复杂主题，这三篇文章只触及到它的皮毛。
Designing a dimensional data warehouse is a complex subject, and I have just touched the surface of it in these three articles.
数据摄取应用程序设计应该容纳一个活动的数据仓库环境，其中所有工作负载都应该处于在线状态并能并发地运行。
The data ingest application design should accommodate an active warehousing environment where all workloads are intended to run online and concurrently.
阶段3：设计数据仓库逻辑模型。
数据仓库应该是为数据分析而设计的。
根据定义，数据市场是数据仓库的一个子集，是专门针对特定的用户群或特定的主题领域设计的。
By definition, a data mart is a subset of a data warehouse designed for a specific group of users or a particular subject area.
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