维度建模宝典：数据仓库工具包第二版

需积分: 49 136 浏览量更新于2024-07-29 收藏 4.46MB PDF 举报

《数据仓库工具包》第二版是由John Wiley & Sons, Inc.出版的一本权威著作，该书由Ralph Kimball和Margy Ross合著。这是一本全面指导数据仓库建模的宝典，专为IT专业人士设计，特别是那些在数据仓库领域寻求深入了解和实践经验的人。书中强调的核心内容是维度建模，这是一种在数据仓库设计中广泛应用的方法论，旨在提升数据分析的效率和准确性。在本书中，作者们深入解析了数据仓库的基本概念，涵盖了从数据仓库的需求分析、概念设计、逻辑设计到物理实施的全过程。他们提倡使用维度模型，这种模型通过将业务实体拆分为事实和维度，使数据结构更加直观，有助于支持复杂的查询和多维分析。维度模型的优势在于它能简化复杂度，提高查询性能，并且促进了数据的易理解性和一致性。作者Ralph Kimball以其在数据仓库领域的深厚经验和影响力，提供了丰富的实践案例和最佳实践，帮助读者掌握如何构建高效的数据仓库架构。同时，书中的第二版可能包含了对新技术和工具的更新，如Oracle数据仓库工具包，这可能是针对Oracle数据库环境下的具体应用进行优化和指导的。《数据仓库工具包》第二版不仅适合数据仓库项目的设计师、分析师和开发人员阅读，也是数据管理团队和企业决策者进行战略规划的重要参考资料。此外，书中还可能涉及到了版权信息，提醒读者关于商标使用的规范，并声明了该书的印刷材料采用的是无酸纸，以保护内容长期保存。这本书是数据仓库领域不可或缺的实战指南，对于希望在这个快速发展的技术领域保持竞争力的专业人士来说，是一份不可多得的学习资料。无论是理论知识还是实战技巧，都能在此找到深入且实用的内容。

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

剩余446页未读，继续阅读

finalarrow

粉丝: 2

维度建模宝典：数据仓库工具包第二版

数据仓库工具包part2

微软数据仓库工具包指南

Hive数据仓库工具详解：体系架构与优势

数据仓库工具箱合集3in1——Data Warehouse Toolkit 3 in 1

数据仓库教程

数据仓库工具箱-kimball：高效数仓建模指南

Hive数据仓库工具详解：原理、优缺点与架构

数据仓库工具箱：深入维度建模指南第二版

第3版《数据仓库工具箱》深度解读维度建模

维度建模全面指南：数据仓库工具箱第3版权威解读

最新资源