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E-raamat: Data Warehousing Fundamentals for IT Professionals

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Data Warehousing Fundamentals for IT ProfessionalsSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 20-Sep-2011
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781118211304
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Data warehousing has revolutionized the way businesses in a wide variety of industries perform analysis and make strategic decisions. Since the first edition of Data Warehousing Fundamentals, numerous enterprises have implemented data warehouse systems and reaped enormous benefits. Many more are in the process of doing so. Now, this new, revised edition covers the essential fundamentals of data warehousing and business intelligence as well as significant recent trends in the field.

The author provides an enhanced, comprehensive overview of data warehousing together with in-depth explanations of critical issues in planning, design, deployment, and ongoing maintenance. IT professionals eager to get into the field will gain a clear understanding of techniques for data extraction from source systems, data cleansing, data transformations, data warehouse architecture and infrastructure, and the various methods for information delivery.

This practical Second Edition highlights the areas of data warehousing and business intelligence where high-impact technological progress has been made. Discussions on developments include data marts, real-time information delivery, data visualization, requirements gathering methods, multi-tier architecture, OLAP applications, Web clickstream analysis, data warehouse appliances, and data mining techniques. The book also contains review questions and exercises for each chapter, appropriate for self-study or classroom work, industry examples of real-world situations, and several appendices with valuable information.

Specifically written for professionals responsible for designing, implementing, or maintaining data warehousing systems, Data Warehousing Fundamentals presents agile, thorough, and systematic development principles for the IT professional and anyone working or researching in information management.

Cutting-edge content and guidance from a data warehousing expert—now expanded to reflect field trends

Data warehousing has revolutionized the way businesses in a wide variety of industries perform analysis and make strategic decisions. Since the first edition of Data Warehousing Fundamentals, numerous enterprises have implemented data warehouse systems and reaped enormous benefits. Many more are in the process of doing so. Now, this new, revised edition covers the essential fundamentals of data warehousing and business intelligence as well as significant recent trends in the field.

The author provides an enhanced, comprehensive overview of data warehousing together with in-depth explanations of critical issues in planning, design, deployment, and ongoing maintenance. IT professionals eager to get into the field will gain a clear understanding of techniques for data extraction from source systems, data cleansing, data transformations, data warehouse architecture and infrastructure, and the various methods for information delivery.

This practical Second Edition highlights the areas of data warehousing and business intelligence where high-impact technological progress has been made. Discussions on developments include data marts, real-time information delivery, data visualization, requirements gathering methods, multi-tier architecture, OLAP applications, Web clickstream analysis, data warehouse appliances, and data mining techniques. The book also contains review questions and exercises for each chapter, appropriate for self-study or classroom work, industry examples of real-world situations, and several appendices with valuable information.

Specifically written for professionals responsible for designing, implementing, or maintaining data warehousing systems, Data Warehousing Fundamentals presents agile, thorough, and systematic development principles for the IT professional and anyone working or researching in information management.

Arvustused

"... clearly the author is a long-time practitioner and expert in this area. And if this were not enough, the language is clear and precise, and the associated content is easily internalised." (BCS, August 2010)

Preface xxv
PART 1 OVERVIEW AND CONCEPTS
1(70)
1 The Compelling Need for Data Warehousing
3(20)
Chapter Objectives
3(1)
Escalating Need for Strategic Information
4(5)
The Information Crisis
6(1)
Technology Trends
6(2)
Opportunities and Risks
8(1)
Failures of Past Decision-Support Systems
9(2)
History of Decision-Support Systems
10(1)
Inability to Provide Information
10(1)
Operational Versus Decision-Support Systems
11(2)
Making the Wheels of Business Turn
12(1)
Watching the Wheels of Business Turn
12(1)
Different Scope, Different Purposes
12(1)
Data Warehousing---The Only Viable Solution
13(2)
A New Type of System Environment
13(1)
Processing Requirements in the New Environment
14(1)
Strategic Information from the Data Warehouse
14(1)
Data Warehouse Defined
15(2)
A Simple Concept for Information Delivery
15(1)
An Environment, Not a Product
15(1)
A Blend of Many Technologies
16(1)
The Data Warehousing Movement
17(1)
Data Warehousing Milestones
17(1)
Initial Challenges
18(1)
Evolution of Business Intelligence
18(2)
BI: Two Environments
19(1)
BI: Data Warehousing and Analytics
19(1)
Chapter Summary
20(1)
Review Questions
20(1)
Exercises
21(2)
2 Data Warehouse: The Building Blocks
23(22)
Chapter Objectives
23(1)
Defining Features
24(5)
Subject-Oriented Data
24(1)
Integrated Data
25(1)
Time-Variant Data
26(1)
Nonvolatile Data
27(1)
Data Granularity
28(1)
Data Warehouses and Data Marts
29(3)
How Are They Different?
29(1)
Top-Down Versus Bottom-Up Approach
29(2)
A Practical Approach
31(1)
Architectural Types
32(2)
Centralized Data Warehouse
32(1)
Independent Data Marts
32(1)
Federated
33(1)
Hub-and-Spoke
33(1)
Data-Mart Bus
34(1)
Overview of the Components
34(7)
Source Data Component
34(3)
Data Staging Component
37(2)
Data Storage Component
39(1)
Information Delivery Component
40(1)
Metadata Component
41(1)
Management and Control Component
41(1)
Metadata in the Data Warehouse
41(1)
Types of Metadata
42(1)
Special Significance
42(1)
Chapter Summary
42(1)
Review Questions
43(1)
Exercises
43(2)
3 Trends in Data Warehousing
45(26)
Chapter Objectives
45(1)
Continued Growth in Data Warehousing
46(4)
Data Warehousing has Become Mainstream
46(1)
Data Warehouse Expansion
47(1)
Vendor Solutions and Products
48(2)
Significant Trends
50(14)
Real-Time Data Warehousing
50(1)
Multiple Data Types
50(2)
Data Visualization
52(2)
Parallel Processing
54(2)
Data Warehouse Appliances
56(1)
Query Tools
56(1)
Browser Tools
57(1)
Data Fusion
57(1)
Data Integration
58(1)
Analytics
59(1)
Agent Technology
59(1)
Syndicated Data
60(1)
Data Warehousing and ERP
60(1)
Data Warehousing and KM
61(2)
Data Warehousing and CRM
63(1)
Agile Development
63(1)
Active Data Warehousing
64(1)
Emergence of Standards
64(2)
Metadata
65(1)
OLAP
65(1)
Web-Enabled Data Warehouse
66(3)
The Warehouse to the Web
67(1)
The Web to the Warehouse
67(2)
The Web-Enabled Configuration
69(1)
Chapter Summary
69(1)
Review Questions
69(1)
Exercises
70(1)
PART 2 PLANNING AND REQUIREMENTS
71(68)
4 Planning and Project Management
73(26)
Chapter Objectives
73(1)
Planning Your Data Warehouse
74(5)
Key Issues
74(2)
Business Requirements, Not Technology
76(1)
Top Management Support
77(1)
Justifying Your Data Warehouse
77(1)
The Overall Plan
78(1)
The Data Warehouse Project
79(4)
How is it Different?
79(2)
Assessment of Readiness
81(1)
The Life-Cycle Approach
81(2)
The Development Phases
83(2)
Adopting Agile Development
84(1)
The Project Team
85(5)
Organizing the Project Team
85(1)
Roles and Responsibilities
86(1)
Skills and Experience Levels
87(1)
User Participation
88(2)
Project Management Considerations
90(6)
Guiding Principles
91(1)
Warning Signs
92(1)
Success Factors
92(1)
Anatomy of a Successful Project
93(1)
Adopt a Practical Approach
94(2)
Chapter Summary
96(1)
Review Questions
96(1)
Exercises
97(2)
5 Defining the Business Requirements
99(22)
Chapter Objectives
99(1)
Dimensional Analysis
100(3)
Usage of Information Unpredictable
100(1)
Dimensional Nature of Business Data
101(1)
Examples of Business Dimensions
102(1)
Infromation Packages---A Useful Concept
103(6)
Requirements Not Fully Determinate
104(1)
Business Dimensions
105(1)
Dimension Hierarchies and Categories
106(1)
Key Business Metrics or Facts
107(2)
Requirements Gathering Methods
109(7)
Types of Questions
110(1)
Arrangement of Questions
111(1)
Interview Techniques
111(2)
Adapting the JAD Methodology
113(2)
Using Questionnaires
115(1)
Review of Existing Documentation
115(1)
Requirements Definition: Scope and Content
116(3)
Data Sources
117(1)
Data Transformation
117(1)
Data Storage
117(1)
Information Delivery
118(1)
Information Package Diagrams
118(1)
Requirements Definition Document Outline
118(1)
Chapter Summary
119(1)
Review Questions
119(1)
Exercises
120(1)
6 Requirements as the Driving Force for Data Warehousing
121(18)
Chapter Objectives
121(1)
Data Design
122(3)
Structure for Business Dimensions
123(1)
Structure for Key Measurements
124(1)
Levels of Detail
125(1)
The Architectural Plan
125(6)
Composition of the Components
126(1)
Special Considerations
127(2)
Tools and Products
129(2)
Data Storage Specifications
131(2)
DBMS Selection
132(1)
Storage Sizing
132(1)
Information Delivery Strategy
133(3)
Queries and Reports
134(1)
Types of Analysis
134(1)
Information Distribution
135(1)
Real Time Information Delivery
135(1)
Decision Support Applications
135(1)
Growth and Expansion
136(1)
Chapter Summary
136(1)
Review Questions
136(1)
Exercises
137(2)
PART 3 ARCHITECTURE AND INFRASTRUCTURE
139(84)
7 Architectural Components
141(22)
Chapter Objectives
141(1)
Understanding Data Warehouse Architecture
141(2)
Architecture: Definitions
142(1)
Architecture in Three Major Areas
142(1)
Distinguishing Characteristics
143(3)
Different Objectives and Scope
144(1)
Data Content
144(1)
Complex Analysis and Quick Response
145(1)
Flexible and Dynamic
145(1)
Metadata-Driven
146(1)
Architectural Framework
146(2)
Architecture Supporting Flow of Data
146(1)
The Management and Control Module
147(1)
Technical Architecture
148(8)
Data Acquisition
149(3)
Data Storage
152(2)
Information Delivery
154(2)
Architectural Types
156(4)
Centralized Corporate Data Warehouse
156(1)
Independent Data Marts
156(3)
Federated
159(1)
Hub-and-Spoke
159(1)
Data-Mart Bus
160(1)
Chapter Summary
160(1)
Review Questions
160(1)
Exercises
161(2)
8 Infrastructure as the Foundation for Data Warehousing
163(30)
Chapter Objectives
163(1)
Infrastructure Supporting Architecture
164(2)
Operational Infrastructure
165(1)
Physical Infrastructure
165(1)
Hardware and Operating Systems
166(15)
Mainframes
167(1)
Open System Servers
168(1)
NT Servers
168(1)
Platform Options
168(9)
Server Hardware
177(4)
Database Software
181(3)
Parallel Processing Options
182(2)
Selection of the DBMS
184(1)
Collection of Tools
184(4)
Architecture First, Then Tools
186(1)
Data Modeling
186(1)
Data Extraction
187(1)
Data Transformation
187(1)
Data Loading
187(1)
Data Quality
187(1)
Queries and Reports
187(1)
Dashboards
187(1)
Scorecards
187(1)
Online Analytical Processing (OLAP)
188(1)
Alert Systems
188(1)
Middleware and Connectivity
188(1)
Data Warehouse Administration
188(1)
Data Warehouse Appliances
188(3)
Evolution of DW Appliances
189(1)
Benefits of DW Appliances
190(1)
Chapter Summary
191(1)
Review Questions
191(1)
Exercises
192(1)
9 The Significant Role of Metadata
193(30)
Chapter Objectives
193(1)
Why Metadata is Important
193(10)
A Critical Need in the Data Warehouse
195(3)
Why Metadata Is Vital For End-Users
198(1)
Why Metadata Is Essential for IT
199(1)
Automation of Warehousing Tasks
200(2)
Establishing the Context of Information
202(1)
Metadata Types by Functional Areas
203(4)
Data Acquisition
204(1)
Data Storage
205(1)
Information Delivery
206(1)
Business Metadata
207(2)
Content Overview
207(1)
Examples of Business Metadata
208(1)
Content Highlights
209(1)
Who Benefits?
209(1)
Technical Metadata
209(3)
Content Overview
210(1)
Examples of Technical Metadata
210(1)
Content Highlights
211(1)
Who Benefits?
211(1)
How to Provide Metadata
212(7)
Metadata Requirements
212(2)
Sources of Metadata
214(1)
Challenges for Metadata Management
215(1)
Metadata Repository
215(2)
Metadata Integration and Standards
217(1)
Implementation Options
218(1)
Chapter Summary
219(1)
Review Questions
220(1)
Exercises
220(3)
PART 4 DATA DESIGN AND DATA PREPARATION
223(116)
10 Principles of Dimensional Modeling
225(24)
Chapter Objectives
225(1)
From Requirements to Data Design
225(7)
Design Decisions
226(1)
Dimensional Modeling Basics
226(4)
E-R Modeling Versus Dimensional Modeling
230(2)
Use of CASE Tools
232(1)
The Star Schema
232(7)
Review of a Simple Star Schema
232(2)
Inside a Dimension Table
234(2)
Inside the Fact Table
236(2)
The Factless Fact Table
238(1)
Data Granularity
238(1)
Star Schema Keys
239(2)
Primary Keys
239(1)
Surrogate Keys
240(1)
Foreign Keys
240(1)
Advantages of the Star Schema
241(3)
Easy for Users to Understand
241(1)
Optimizes Navigation
242(1)
Most Suitable for Query Processing
243(1)
Starjoin and Starindex
244(1)
Star Schema: Examples
244(2)
Video Rental
244(1)
Supermarket
244(1)
Wireless Phone Service
244(1)
Auction Company
244(2)
Chapter Summary
246(1)
Review Questions
247(1)
Exercises
247(2)
11 Dimensional Modeling: Advanced Topics
249(32)
Chapter Objectives
249(1)
Updates to the Dimension Tables
250(5)
Slowly Changing Dimensions
250(1)
Type 1 Changes: Correction of Errors
251(1)
Type 2 Changes: Preservation of History
252(1)
Type 3 Changes: Tentative Soft Revisions
253(2)
Miscellaneous Dimensions
255(4)
Large Dimensions
255(1)
Rapidly Changing Dimensions
256(2)
Junk Dimensions
258(1)
The Snowflake Schema
259(3)
Options to Normalize
259(1)
Advantages and Disadvantages
260(2)
When to Snowflake
262(1)
Aggregate Fact Tables
262(10)
Fact Table Sizes
264(2)
Need for Aggregates
266(1)
Aggregating Fact Tables
266(5)
Aggregation Options
271(1)
Families of Stars
272(5)
Snapshot and Transaction Tables
273(1)
Core and Custom Tables
274(1)
Supporting Enterprise Value Chain or Value Circle
274(1)
Conforming Dimensions
275(1)
Standardizing Facts
276(1)
Summary of Family of STARS
277(1)
Chapter Summary
277(1)
Review Questions
278(1)
Exercises
278(3)
12 Data Extraction, Transformation, and Loading
281(34)
Chapter Objectives
281(1)
Etl Overview
282(2)
Most Important and Most Challenging
282(1)
Time Consuming and Arduous
283(1)
Etl Requirements and Steps
284(2)
Key Factors
285(1)
Data Extraction
286(9)
Source Identification
287(1)
Data Extraction Techniques
287(7)
Evaluation of the Techniques
294(1)
Data Transformation
295(7)
Data Transformation: Basic Tasks
296(1)
Major Transformation Types
297(2)
Data Integration and Consolidation
299(2)
Transformation for Dimension Attributes
301(1)
How to Implement Transformation
301(1)
Data Loading
302(6)
Applying Data: Techniques and Processes
303(3)
Data Refresh Versus Update
306(1)
Procedure for Dimension Tables
306(1)
Fact Tables: History and Incremental Loads
307(1)
Etl Summary
308(3)
Etl Tool Options
308(1)
Reemphasizing Etl Metadata
309(1)
Etl Summary and Approach
310(1)
Other Integration Approaches
311(2)
Enterprise Information Integration (EII)
311(1)
Enterprise Application Integration (EAI)
312(1)
Chapter Summary
313(1)
Review Questions
313(1)
Exercises
314(1)
13 Data Quality: A Key to Success
315(24)
Chapter Objectives
315(1)
Why is Data Quality Critical?
316(7)
What Is Data Quality?
316(3)
Benefits of Improved Data Quality
319(1)
Types of Data Quality Problems
320(3)
Data Quality Challenges
323(3)
Sources of Data Pollution
323(2)
Validation of Names and Addresses
325(1)
Costs of Poor Data Quality
325(1)
Data Quality Tools
326(2)
Categories of Data Cleansing Tools
327(1)
Error Discovery Features
327(1)
Data Correction Features
327(1)
The DBMS for Quality Control
327(1)
Data Quality Initiative
328(7)
Data Cleansing Decisions
329(1)
Who Should Be Responsible?
330(3)
The Purification Process
333(1)
Practical Tips on Data Quality
334(1)
Master Data Management (MDM)
335(1)
MDM Categories
335(1)
MDM Benefits
335(1)
MDM and Data Warehousing
336(1)
Chapter Summary
336(1)
Review Questions
336(1)
Exercises
337(2)
PART 5 INFORMATION ACCESS AND DELIVERY
339(122)
14 Matching Information to the Classes of Users
341(32)
Chapter Objectives
341(1)
Information From the Data Warehouse
342(7)
Data Warehouse Versus Operational Systems
342(2)
Information Potential
344(3)
User-Information Interface
347(1)
Industry Applications
348(1)
Who Will Use The Information?
349(7)
Classes of Users
349(3)
What They Need
352(2)
How to Provide Information
354(2)
Information Delivery
356(4)
Queries
357(1)
Reports
358(1)
Analysis
359(1)
Applications
359(1)
Information Delivery Tools
360(6)
The Desktop Environment
360(1)
Methodology for Tool Selection
361(3)
Tool Selection Criteria
364(1)
Information Delivery Framework
365(1)
Information Delivery: Special Topics
366(5)
Business Activity Monitoring (BAM)
366(1)
Dashboards and Scorecards
367(4)
Chapter Summary
371(1)
Review Questions
371(1)
Exercises
372(1)
15 Olap in the Data Warehouse
373(34)
Chapter Objectives
373(1)
Demand for Online Analytical Processing
374(8)
Need for Multidimensional Analysis
374(1)
Fast Access and Powerful Calculations
375(2)
Limitations of Other Analysis Methods
377(2)
OLAP is the Answer
379(1)
OLAP Definitions and Rules
379(3)
OLAP Characteristics
382(1)
Major Features and Functions
382(11)
General Features
383(1)
Dimensional Analysis
383(3)
What Are Hypercubes?
386(4)
Drill Down and Roll Up
390(2)
Slice and Dice or Rotation
392(1)
Uses and Benefits
393(1)
OLAP Models
393(5)
Overview of Variations
394(1)
The MOLAP Model
394(1)
The ROLAP Model
395(2)
ROLAP Versus MOLAP
397(1)
OLAP Implementation Considerations
398(6)
Data Design and Preparation
399(2)
Administration and Performance
401(1)
OLAP Platforms
402(1)
OLAP Tools and Products
402(1)
Implementation Steps
403(1)
Examples of Typical Implementations
404(1)
Chapter Summary
404(1)
Review Questions
405(1)
Exercises
405(2)
16 Data Warehousing and the Web
407(22)
Chapter Objectives
407(1)
Web-Enabled Data Warehouse
408(6)
Why the Web?
408(2)
Convergence of Technologies
410(1)
Adapting the Data Warehouse for the Web
411(1)
The Web as a Data Source
412(1)
Clickstream Analysis
413(1)
Web-Based Information Delivery
414(6)
Expanded Usage
414(2)
New Information Strategies
416(2)
Browser Technology for the Data Warehouse
418(1)
Security Issues
419(1)
OLAP AND THE WEB
420(1)
Enterprise OLAP
420(1)
Web-OLAP Approaches
420(1)
OLAP Engine Design
421(1)
Building a Web-Enabled Data Warehouse
421(5)
Nature of the Data Webhouse
422(1)
Implementation Considerations
423(1)
Putting the Pieces Together
424(2)
Web Processing Model
426(1)
Chapter Summary
426(1)
Review Questions
426(1)
Exercises
427(2)
17 Data Mining Basics
429(32)
Chapter Objectives
429(1)
What is Data Mining?
430(9)
Data Mining Defined
431(1)
The Knowledge Discovery Process
432(3)
OLAP Versus Data Mining
435(1)
Some Aspects of Data Mining
436(2)
Data Mining and the Data Warehouse
438(1)
Major Data Mining Techniques
439(13)
Cluster Detection
440(3)
Decision Trees
443(1)
Memory-Based Reasoning
444(1)
Link Analysis
445(2)
Neural Networks
447(1)
Genetic Algorithms
448(2)
Moving into Data Mining
450(2)
Data Mining Applications
452(7)
Benefits of Data Mining
453(1)
Applications in CRM (Customer Relationship Management)
454(1)
Applications in the Retail Industry
455(1)
Applications in the Telecommunications Industry
456(1)
Applications in Biotechnology
457(2)
Applications in Banking and Finance
459(1)
Chapter Summary
459(1)
Review Questions
459(1)
Exercises
460(1)
PART 6 IMPLEMENTATION AND MAINTENANCE
461(66)
18 The Physical Design Process
463(26)
Chapter Objectives
463(1)
Physical Design Steps
464(3)
Develop Standards
464(1)
Create Aggregates Plan
465(1)
Determine the Data Partitioning Scheme
465(1)
Establish Clustering Options
466(1)
Prepare an Indexing Strategy
466(1)
Assign Storage Structures
466(1)
Complete Physical Model
467(1)
Physical Design Considerations
467(6)
Physical Design Objectives
467(2)
From Logical Model to Physical Model
469(1)
Physical Model Components
469(1)
Significance of Standards
470(3)
Physical Storage
473(4)
Storage Area Data Structures
473(1)
Optimizing Storage
473(3)
Using RAID Technology
476(1)
Estimating Storage Sizes
477(1)
Indexing the Data Warehouse
477(6)
Indexing Overview
477(2)
B-Tree Index
479(2)
Bitmapped Index
481(1)
Clustered Indexes
482(1)
Indexing the Fact Table
482(1)
Indexing the Dimension Tables
483(1)
Performance Enhancement Techniques
483(3)
Data Partitioning
483(1)
Data Clustering
484(1)
Parallel Processing
484(1)
Summary Levels
485(1)
Referential Integrity Checks
485(1)
Initialization Parameters
485(1)
Data Arrays
486(1)
Chapter Summary
486(1)
Review Questions
486(1)
Exercises
487(2)
19 Data Warehouse Deployment
489(22)
Chapter Objectives
489(1)
Data Warehouse Testing
490(1)
Front-End
490(1)
ETL Testing
490(1)
Major Deployment Activities
491(6)
Complete User Acceptance
491(1)
Perform Initial Loads
492(1)
Get User Desktops Ready
493(1)
Complete Initial User Training
494(1)
Institute Initial User Support
495(1)
Deploy in Stages
495(2)
Considerations for a Pilot
497(5)
When is a Pilot Data Mart Useful?
497(1)
Types of Pilot Projects
498(2)
Choosing the Pilot
500(1)
Expanding and Integrating the Pilot
501(1)
Security
502(2)
Security Policy
502(1)
Managing User Privileges
502(1)
Password Considerations
503(1)
Security Tools
504(1)
Backup and Recovery
504(4)
Why Back Up the Data Warehouse?
505(1)
Backup Strategy
505(1)
Setting up a Practical Schedule
506(1)
Recovery
507(1)
Chapter Summary
508(1)
Review Questions
508(1)
Exercises
509(2)
20 Growth and Maintenance
511(16)
Chapter Objectives
511(1)
Monitoring the Data Warehouse
512(3)
Collection of Statistics
512(2)
Using Statistics for Growth Planning
514(1)
Using Statistics for Fine-Tuning
514(1)
Publishing Trends for Users
515(1)
User Training and Support
515(5)
User Training Content
516(1)
Preparing the Training Program
516(2)
Delivering the Training Program
518(1)
User Support
519(1)
Managing the Data Warehouse
520(4)
Platform Upgrades
521(1)
Managing Data Growth
521(1)
Storage Management
522(1)
ETL Management
522(1)
Data Model Revisions
523(1)
Information Delivery Enhancements
523(1)
Ongoing Fine-Tuning
524(1)
Chapter Summary
524(1)
Review Questions
525(1)
Exercises
525(2)
Answers to Selected Exercises 527(4)
Appendix A Project Life Cycle Steps and Checklists 531(4)
Appendix B Critical Factors for Success 535(2)
Appendix C Guidelines for Evaluating Vendor Solutions 537(2)
Appendix D Highlights of Vendors and Products 539(10)
Appendix E Real-World Examples of Best Practices 549(6)
References 555(2)
Glossary 557(8)
Index 565
PAULRAJ PONNIAH, PHD, with over thirty years of experience as an IT consultant, has worked with such organizations as Texaco, Sotheby's, Blue Cross/Blue Shield, NA Philips, New York-Presbyterian Hospital, Panasonic, and Bantam Doubleday Dell. He specializes in the design and implementation of data warehouse and database systems. Dr. Ponniah has published three successful books and, as Adjunct Professor, continues to teach college courses in data warehousing and database design.
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