数据仓库构建：第三版精华解读

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"《数据仓库构建》是W.H.Inmon所著的一本关于数据仓库建设的书籍，已更新至第三版。该书由John Wiley & Sons, Inc. 出版，属于Wiley Computer Publishing系列。书中详细介绍了如何构建数据仓库，并涵盖了与数据仓库相关的各种技术和实践方法。" 在构建数据仓库的过程中，作者W.H. Inmon是一位权威的专家，他提出的概念和方法对业界有着深远的影响。数据仓库是企业级系统中用于存储、管理和分析大量历史数据的平台，主要用于支持决策制定和业务洞察。这个过程通常涉及以下几个关键知识点： 1. **数据仓库定义**：数据仓库是一个集成的、非易失的、面向主题的系统，用于支持管理决策。它不同于操作型数据库，后者专注于事务处理，而数据仓库专注于数据分析。 2. **数据抽取（ETL）**：提取（Extract）、转换（Transform）、加载（Load）是构建数据仓库的关键步骤。ETL过程从源系统抽取数据，进行清洗、转换，然后加载到数据仓库中，确保数据的一致性和准确性。 3. **数据清洗**：在数据进入数据仓库之前，必须进行数据清洗，消除错误、不一致和重复的数据，以提高数据质量。 4. **数据模型设计**：Inmon推崇的是“自顶向下”的数据仓库建模方法，强调先建立企业级的架构，然后逐步细化到各个业务领域。 5. **星型和雪花型模式**：数据仓库中常见的两种物理模型，星型模式由一个事实表和多个维度表组成，结构简单，查询效率高；雪花型模式是在星型模式基础上对维度表进行规范化，减少数据冗余，但可能增加查询复杂性。 6. **数据分层**：数据仓库通常包括ODS（Operational Data Store）、DWH（Data Warehouse）和DW（Data Mart）等层次，分别用于不同目的，如ODS用于快速响应操作需求，DWH处理整个企业的数据，DW则针对特定部门或业务问题。 7. **性能优化**：为了高效地处理大数据量，数据仓库会采用分区、索引、物化视图等技术来加速查询。 8. **数据安全与访问控制**：保护数据的安全性和隐私，实施用户权限管理，确保只有授权的人员可以访问和分析数据。 9. **数据仓库生命周期管理**：从需求分析、设计、实现到维护，数据仓库有其完整的生命周期，需要定期评估和调整以适应业务变化。 10. **商业智能（BI）工具**：数据仓库往往与BI工具结合，如Tableau、Power BI等，用于生成报告、仪表板和高级分析，帮助决策者解读数据仓库中的信息。《数据仓库构建》第三版是深入理解和实践数据仓库建设的重要参考资料，涵盖了从理论到实践的全面知识，对于IT专业人员来说，无论是设计、开发还是管理数据仓库，都能从中获益。

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.