数据仓库建设与模型设计：W.H.Inmon的第三版指南

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"《Building the Data Warehouse》是W.H. Inmon所著的关于数据仓库建设的第三版书籍，由John Wiley & Sons, Inc. 出版。这本书深入探讨了DW（数据仓库）的设计和构建过程，是数据仓库领域的经典著作之一。" 数据仓库（DW）是企业级的数据存储系统，它的主要目的是为了支持决策制定，提供单一的、一致的、准确的数据视图。数据仓库通常从多个在线事务处理（OLTP）系统中提取数据，并经过清洗、转换和整合，以便进行分析和报告。在数据仓库的建模过程中，Inmon提出了概念数据模型（Conceptual Data Model），这是数据仓库设计的核心。这个模型强调了业务实体及其之间的关系，帮助定义和组织数据仓库中的信息。它通常包括实体（如客户、产品、订单等）、属性（描述实体特征的字段）和联系（描述实体间的关系）。通过这种建模方法，可以确保数据仓库的结构清晰，易于理解和使用。此外，Inmon还强调了自顶向下的设计方法，即先构建整个数据仓库的全局视角，然后逐步细化到各个部分。这种方法强调了对业务流程的理解，确保数据仓库与企业的业务需求紧密匹配。数据仓库的构建通常包括以下步骤：需求分析、源系统分析、数据建模、数据抽取、数据转换、数据加载以及持续的数据维护和更新。数据仓库的建设还包括选择合适的技术架构，例如星型或雪花型的维度建模，以及使用ETL（Extract, Transform, Load）工具来处理数据的抽取、转换和加载。ETL过程是数据仓库生命周期的关键部分，它负责从源系统获取数据，清洗去除错误和不一致性，然后将数据加载到数据仓库中。此外，数据仓库的性能优化也是重要的考虑因素，包括索引策略、分区技术、并行处理等，以确保数据查询的效率和响应速度。同时，数据安全和访问控制也是必须考虑的方面，以保护敏感信息并确保只有授权用户能访问数据。《Building the Data Warehouse》这本书为读者提供了全面的数据仓库理论和实践知识，无论对于初学者还是经验丰富的专业人士，都是一个宝贵的参考资料。通过学习和应用书中的原则和方法，可以帮助企业和组织构建出高效、可靠的数据仓库系统，从而提升决策质量和业务洞察力。

Introduction

xiii

Databases and database theory have been around for a long time. Early rendi-

tions of databases centered around a single database serving every purpose

known to the information processing community—from transaction to batch

processing to analytical processing. In most cases, the primary focus of the

early database systems was operational—usually transactional—processing. In

recent years, a more sophisticated notion of the database has emerged—one

that serves operational needs and another that serves informational or analyti-

cal needs. To some extent, this more enlightened notion of the database is due

to the advent of PCs, 4GL technology, and the empowerment of the end user.

The split of operational and informational databases occurs for many reasons:

■■

The data serving operational needs is physically different data from that

serving informational or analytic needs.

■■

The supporting technology for operational processing is fundamentally dif-

ferent from the technology used to support informational or analytical

needs.

■■

The user community for operational data is different from the one served

by informational or analytical data.

■■

The processing characteristics for the operational environment and the

informational environment are fundamentally different.

Because of these reasons (and many more), the modern way to build systems is

to separate the operational from the informational or analytical processing and

data.

This book is about the analytical [or the decision support systems (DSS)] envi-

ronment and the structuring of data in that environment. The focus of the book

is on what is termed the “data warehouse” (or “information warehouse”), which

is at the heart of informational, DSS processing.

The discussions in this book are geared to the manager and the developer.

Where appropriate, some level of discussion will be at the technical level. But,

for the most part, the book is about issues and techniques. This book is meant

to serve as a guideline for the designer and the developer.

PREFACE FOR THE SECOND EDITION

xiii

When the first edition of Building the Data Warehouse was printed, the data-

base theorists scoffed at the notion of the data warehouse. One theoretician

stated that data warehousing set back the information technology industry 20

years. Another stated that the founder of data warehousing should not be

allowed to speak in public. And yet another academic proclaimed that data

warehousing was nothing new and that the world of academia had known

about data warehousing all along although there were no books, no articles, no

classes, no seminars, no conferences, no presentations, no references, no

papers, and no use of the terms or concepts in existence in academia at that

time.

When the second edition of the book appeared, the world was mad for anything

of the Internet. In order to be successful it had to be “e” something—e-business,

e-commerce, e-tailing, and so forth. One venture capitalist was known to say,

“Why do we need a data warehouse when we have the Internet?”

But data warehousing has surpassed the database theoreticians who wanted to

put all data in a single database. Data warehousing survived the dot.com disas-

ter brought on by the short-sighted venture capitalists. In an age when technol-

ogy in general is spurned by Wall Street and Main Street, data warehousing has

never been more alive or stronger. There are conferences, seminars, books,

articles, consulting, and the like. But mostly there are companies doing data

warehousing, and making the discovery that, unlike the overhyped New Econ-

omy, the data warehouse actually delivers, even though Silicon Valley is still in

a state of denial.

The third edition of this book heralds a newer and even stronger day for data

warehousing. Today data warehousing is not a theory but a fact of life. New

technology is right around the corner to support some of the more exotic needs

of a data warehouse. Corporations are running major pieces of their business

on data warehouses. The cost of information has dropped dramatically because

of data warehouses. Managers at long last have a viable solution to the ugliness

of the legacy systems environment. For the first time, a corporate “memory” of

historical information is available. Integration of data across the corporation is

a real possibility, in most cases for the first time. Corporations are learning how

PREFACE FOR THE THIRD EDITION

xiv

to go from data to information to competitive advantage. In short, data ware-

housing has unlocked a world of possibility.

One confusing aspect of data warehousing is that it is an architecture, not a

technology. This frustrates the technician and the venture capitalist alike

because these people want to buy something in a nice clean box. But data ware-

housing simply does not lend itself to being “boxed up.” The difference between

an architecture and a technology is like the difference between Santa Fe, New

Mexico, and adobe bricks. If you drive the streets of Santa Fe you know you are

there and nowhere else. Each home, each office building, each restaurant has a

distinctive look that says “This is Santa Fe.” The look and style that make Santa

Fe distinctive are the architecture. Now, that architecture is made up of such

things as adobe bricks and exposed beams. There is a whole art to the making

of adobe bricks and exposed beams. And it is certainly true that you could not

have Santa Fe architecture without having adobe bricks and exposed beams.

But adobe bricks and exposed beams by themselves do not make an architec-

ture. They are independent technologies. For example, you have adobe bricks

throughout the Southwest and the rest of the world that are not Santa Fe

architecture.

Thus it is with architecture and technology, and with data warehousing and

databases and other technology. There is the architecture, then there is the

underlying technology, and they are two very different things. Unquestionably,

there is a relationship between data warehousing and database technology, but

they are most certainly not the same. Data warehousing requires the support of

many different kinds of technology.

With the third edition of this book, we now know what works and what does

not. When the first edition was written, there was some experience with devel-

oping and using warehouses, but truthfully, there was not the broad base of

experience that exists today. For example, today we know with certainty the

following:

■■

Data warehouses are built under a different development methodology

than applications. Not keeping this in mind is a recipe for disaster.

■■

Data warehouses are fundamentally different from data marts. The two do

not mix—they are like oil and water.

■■

Data warehouses deliver on their promise, unlike many overhyped tech-

nologies that simply faded away.

■■

Data warehouses attract huge amounts of data, to the point that entirely

new approaches to the management of large amounts of data are required.

But perhaps the most intriguing thing that has been learned about data ware-

housing is that data warehouses form a foundation for many other forms of

Preface for the Third Edition

processing. The granular data found in the data warehouse can be reshaped and

reused. If there is any immutable and profound truth about data warehouses, it

is that data warehouses provide an ideal foundation for many other forms of

information processing. There are a whole host of reasons why this foundation

is so important:

■■

There is a single version of the truth.

■■

Data can be reconciled if necessary.

■■

Data is immediately available for new, unknown uses.

And, finally, data warehousing has lowered the cost of information in the orga-

nization. With data warehousing, data is inexpensive to get to and fast to

access.