《数据仓库工具包》第二版：完整维度建模指南

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"The Data Warehouse Toolkit" 是一本由 Ralph Kimball 和 Margy Ross 合著的书籍，主要聚焦于数据仓库的构建与维度建模。该书的第二版详细介绍了如何在 MS SQL 2008 R2 环境下进行数据仓库建模。在数据仓库领域，Ralph Kimball 和 Margy Ross 是极具影响力的专家，他们的著作《The Data Warehouse Toolkit》被广泛视为数据仓库设计的经典参考。这本书的核心内容是介绍维度建模，这是一种用于构建数据仓库的结构化方法，旨在优化分析查询性能，提供用户友好的视图，并支持业务智能需求。维度建模的关键概念包括星型模式、雪花模式和事实表以及维度表。星型模式是最常见的数据仓库架构，由一个中心事实表和一组相关联的维度表组成，形状类似于星形。事实表存储测量的业务事件或交易，而维度表则包含描述这些事件的上下文信息。雪花模式是星型模式的一种扩展，通过将共享的维度表分解为更细粒度的子维度表来减少数据冗余，提高数据质量。书中详细阐述了如何设计和实现这些模型，包括如何选择关键性能指标（KPIs）、确定事实和维度、以及创建粒度一致的模型。此外，还讨论了如何处理不规则和缓慢变化维度，这些是数据仓库设计中常见的挑战。书中还会涉及ETL（提取、转换、加载）过程，这是将数据从源系统迁移到数据仓库的重要步骤。 MS SQL 2008 R2 是微软的一个重要数据库管理系统版本，它提供了许多用于数据仓库和商业智能的功能，如Integration Services（用于ETL），Analysis Services（用于OLAP处理和多维数据集），以及Reporting Services（用于报表生成）。作者在书中可能详细解释了如何利用这些工具来实施数据仓库项目。《The Data Warehouse Toolkit》第二版是一个全面的指南，涵盖了数据仓库从规划到实施的各个方面，对于希望深入理解和应用维度建模技术的IT专业人士来说，是一份宝贵的资源。书中提供的最佳实践和案例研究有助于读者更好地理解如何在实际环境中构建高效的数据仓库解决方案。

ACKNOWLEDGMENTS

irst of all, we want to thank the thousands of you who have read our Toolkit

books, attended our courses, and engaged us in consulting projects. We have

learned as much from you as we have taught. As a group, you have had a pro-

foundly positive impact on the data warehousing industry. Congratulations!

This book would not have been written without the assistance of our business

partners. We want to thank Julie Kimball of Ralph Kimball Associates for her

vision and determination in getting the project launched. While Julie was the

catalyst who got the ball rolling, Bob Becker of DecisionWorks Consulting

helped keep it in motion as he drafted, reviewed, and served as a general

sounding board. We are grateful to them both because they helped an enor-

mous amount.

We wrote this book with a little help from our friends, who provided input or

feedback on specific chapters. We want to thank Bill Schmarzo of Decision-

Works, Charles Hagensen of Attachmate Corporation, and Warren Thorn-

thwaite of InfoDynamics for their counsel on Chapters 6, 7, and 16, respectively.

Bob Elliott, our editor at John Wiley & Sons, and the entire Wiley team have

supported this project with skill, encouragement, and enthusiasm. It has been

a pleasure to work with them. We also want to thank Justin Kestelyn, editor-

in-chief at Intelligent Enterprise for allowing us to adapt materials from sev-

eral of Ralph’s articles for inclusion in this book.

To our families, thanks for being there for us when we needed you and for giv-

ing us the time it took. Spouses Julie Kimball and Scott Ross and children Sara

Hayden Smith, Brian Kimball, and Katie Ross all contributed a lot to this book,

often without realizing it. Thanks for your unconditional support.

xvii

INTRODUCTION

he data warehousing industry certainly has matured since Ralph Kimball pub-

lished the first edition of The Data Warehouse Toolkit (Wiley) in 1996. Although

large corporate early adopters paved the way, since then, data warehousing

has been embraced by organizations of all sizes. The industry has constructed

thousands of data warehouses. The volume of data continues to grow as we

populate our warehouses with increasingly atomic data and update them with

greater frequency. Vendors continue to blanket the market with an ever-

expanding set of tools to help us with data warehouse design, development,

and usage. Most important, armed with access to our data warehouses, busi-

ness professionals are making better decisions and generating payback on

their data warehouse investments.

Since the first edition of The Data Warehouse Toolkit was published, dimen-

sional modeling has been broadly accepted as the dominant technique for data

warehouse presentation. Data warehouse practitioners and pundits alike have

recognized that the data warehouse presentation must be grounded in sim-

plicity if it stands any chance of success. Simplicity is the fundamental key that

allows users to understand databases easily and software to navigate data-

bases efficiently. In many ways, dimensional modeling amounts to holding the

fort against assaults on simplicity. By consistently returning to a business-

driven perspective and by refusing to compromise on the goals of user under-

standability and query performance, we establish a coherent design that

serves the organization’s analytic needs. Based on our experience and the

overwhelming feedback from numerous practitioners from companies like

your own, we believe that dimensional modeling is absolutely critical to a suc-

cessful data warehousing initiative.

Dimensional modeling also has emerged as the only coherent architecture for

building distributed data warehouse systems. When we use the conformed

dimensions and conformed facts of a set of dimensional models, we have a

practical and predictable framework for incrementally building complex data

warehouse systems that have no center.

For all that has changed in our industry, the core dimensional modeling tech-

niques that Ralph Kimball published six years ago have withstood the test of

time. Concepts such as slowly changing dimensions, heterogeneous products,

factless fact tables, and architected data marts continue to be discussed in data

warehouse design workshops around the globe. The original concepts have

been embellished and enhanced by new and complementary techniques. We

decided to publish a second edition of Kimball’s seminal work because we felt

that it would be useful to pull together our collective thoughts on dimensional

modeling under a single cover. We have each focused exclusively on decision

support and data warehousing for over two decades. We hope to share the

dimensional modeling patterns that have emerged repeatedly during the

course of our data warehousing careers. This book is loaded with specific,

practical design recommendations based on real-world scenarios.

The goal of this book is to provide a one-stop shop for dimensional modeling

techniques. True to its title, it is a toolkit of dimensional design principles and

techniques. We will address the needs of those just getting started in dimen-

sional data warehousing, and we will describe advanced concepts for those of

you who have been at this a while. We believe that this book stands alone in its

depth of coverage on the topic of dimensional modeling.

Intended Audience

This book is intended for data warehouse designers, implementers, and man-

agers. In addition, business analysts who are active participants in a ware-

house initiative will find the content useful.

Even if you’re not directly responsible for the dimensional model, we believe

that it is important for all members of a warehouse project team to be comfort-

able with dimensional modeling concepts. The dimensional model has an

impact on most aspects of a warehouse implementation, beginning with the

translation of business requirements, through data staging, and finally, to the

unveiling of a data warehouse through analytic applications. Due to the broad

implications, you need to be conversant in dimensional modeling regardless

whether you are responsible primarily for project management, business

analysis, data architecture, database design, data staging, analytic applica-

tions, or education and support. We’ve written this book so that it is accessible

to a broad audience.

For those of you who have read the first edition of this book, some of the famil-

iar case studies will reappear in this edition; however, they have been updated

significantly and fleshed out with richer content. We have developed vignettes

for new industries, including health care, telecommunications, and electronic

commerce. In addition, we have introduced more horizontal, cross-industry

case studies for business functions such as human resources, accounting, pro-

curement, and customer relationship management.

Introduction

xviii

Introduction

xix

The content in this book is mildly technical. We discuss dimensional modeling

in the context of a relational database primarily. We presume that readers have

basic knowledge of relational database concepts such as tables, rows, keys,

and joins. Given that we will be discussing dimensional models in a non-

denominational manner, we won’t dive into specific physical design and

tuning guidance for any given database management systems.

Chapter Preview

The book is organized around a series of business vignettes or case studies. We

believe that developing the design techniques by example is an extremely

effective approach because it allows us to share very tangible guidance. While

not intended to be full-scale application or industry solutions, these examples

serve as a framework to discuss the patterns that emerge in dimensional mod-

eling. In our experience, it is often easier to grasp the main elements of a

design technique by stepping away from the all-too-familiar complexities of

one’s own applications in order to think about another business. Readers of

the first edition have responded very favorably to this approach.

The chapters of this book build on one another. We will start with basic con-

cepts and introduce more advanced content as the book unfolds. The chapters

are to be read in order by every reader. For example, Chapter 15 on insurance

will be difficult to comprehend unless you have read the preceding chapters

on retailing, procurement, order management, and customer relationship

management.

Those of you who have read the first edition may be tempted to skip the first

few chapters. While some of the early grounding regarding facts and dimen-

sions may be familiar turf, we don’t want you to sprint too far ahead. For

example, the first case study focuses on the retailing industry, just as it did in

the first edition. However, in this edition we advocate a new approach, mak-

ing a strong case for tackling the atomic, bedrock data of your organization.

You’ll miss out on this rationalization and other updates to fundamental con-

cepts if you skip ahead too quickly.

Navigation Aids

We have laced the book with tips, key concepts, and chapter pointers to make

it more usable and easily referenced in the future. In addition, we have pro-

vided an extensive glossary of terms.

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