Data Warehousing Fundamentals for IT Professionals

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 researc hing in information management.

Spis treści:
Preface.
Part 1 OVERVIEW AND CONCEPTS.
1 The Compelling Need for Data Warehousing.
Chapter Objectives.
Escalating Need for Strategic Information.
Failures of Past Decision Support Systems.
Operational Versus Decision–Support Systems.
Data Warehousing––The Only Viable Solution.
Data Warehouse Defined.
The Data Warehousing Movement.
Evolution of Business Intelligence.
Chapter Summary.
Review Questions.
Exercises.
2 Data Warehouse: The Building Blocks.
Chapter Objectives.
Defining Features.
Data Warehouses and Data Marts.
Architectural Types.
Overview of Components.
Metadata in the Data Warehouse.
Chapter Summary.
Review Questions.
Exercises.
3 Trends in Data Warehousing.
Chapter Objectives.
Continued Growth in Data Warehousing.
Data Warehousing has Become Mainstream.
Data Warehouse Expansion.
Vendor Solutions and Products.
Significant Trends.
Real–Time Data Warehousing.
Multiple Data Types.
Data Visualization.
Emergence of Standards.
Web–Enabled Data Warehouse.
Chapter Summary.
Review Questions.
Exercises.
Part 2 PLANNING AND REQUIREMENTS.
4 Planning and Project Management.
Chapter Objectives.
Planning Your Data Warehouse.
The Data Warehouse Project.
The Project Team.
Project Management Considerations.
Chapter Summary.
Review Questions.
Exercises.
5 Defining the Business Requirements.
Chapter Objectives.
Dimensional Analysis.
Information Packages?A Useful Concept.
Requirements Gathering Methods.
Requirements Definition: Scope and Content.
Chapter Summary.
Review Questions.
Exercises.
6 Requirements as the Driving Force for Data Warehousing.
Chapter Objectives.
Data Design.
The Architectural Plan.
Data Storage Specifications.
Inform ation Delivery Strategy.
Chapter Summary.
Review Questions.
Exercises.
Part 3 ARCHITECTURE AND INFRASTRUCTURE.
7 The Architectural Components.
Chapter Objectives.
Understanding Data Warehouse Architecture.
Distinguishing Characteristics.
Architectural Framework.
Technical Architecture.
Architectural Types.
Chapter Summary.
Review Questions.
Exercises.
8 Infrastructure as the Foundation for Data Warehousing.
Chapter Objectives.
Infrastructure Supporting Architecture.
Hardware and Operating Systems.
Database Software.
Collection of Tools.
Data Warehouse Appliances.
Chapter Summary.
Review Questions.
Exercises.
9 The Significant Role of Metadata.
Chapter Objectives.
Why Metadata is Important.
Metadata Types by Functional Areas.
Business Metadata.
Technical Metadata.
How to Provide Metadata.
Chapter Summary.
Review Questions.
Exercises.
Part 4 DATA DESIGN AND DATA PREPARATION.
10 Principles of Dimensional Modeling.
Chapter Objectives.
From Requirements to Data Design.
The STAR Schema.
STAR Schema Keys.
Advantages of the STAR Schema.
STAR Schema: Examples.
Chapter Summary.
Review Questions.
Exercises.
11 Dimensional Modeling: Advanced Topics.
Chapter Objectives.
Updates to the Dimension Tables.
Miscellaneous Dimensions.
The Snowflake Schema.
Aggregate Fact Tables.
Families of STARS.
Chapter Summary.
Review Questions.
Exercises.
12 Data Extraction, Transformation, and Loading.
Chapter Objectives.
ETL Overview.
Data Extraction.
Data Transformation.
Data Loading.
ETL Summary.
Other Integration Approaches.
Chapter Summary.
Review Questions.
Exercises.
13 Data Quality: A key to Success.
Chapter Objectives.
Why is Data Quality Critical?.
Data Quality Challenges.
Data Quality Tools.
Data Quality Initiat ive.
Master Data Management.
Chapter Summary.
Review Questions.
Exercises.
Part 5 INFORMATION ACCESS AND DELIVERY.
14 Matching Information to the Classes of Users.
Chapter Objectives.
Information from the Data Warehouse.
Who Will Use the Information?.
Information Delivery.
Information Delivery Tools.
Information Delivery: Special Topics.
Chapter Summary.
Review Questions.
Exercises.
15 OLAP in the Data Warehouse.
Chapter Objectives.
Demand for Online Analytical Processing.
Major Features and Functions.
OLAP Models.
OLAP Implementation Considerations.
Chapter Summary.
Review Questions.
Exercises.
16 Data Warehousing and the Web.
Chapter Objectives.
Web–Enabled Data Warehouse.
Web–Based Information Delivery.
OLAP and the Web.
Building a Web–Enabled Data Warehouse.
Chapter Summary.
Review Questions.
Exercises.
17 Data Mining Basics.
Chapter Objectives.
What is Data Mining?.
Major Data Mining Techniques.
Data Mining Applications.
Chapter Summary.
Review Questions.
Exercises.
Part 6 IMPLEMENTATION AND MAINTENANCE.
18 The Physical Design Process.
Chapter Objectives.
Physical Design Steps.
Physical Design Considerations.
Physical Storage.
Indexing the Data Warehouse.
Performance Enhancement Techniques.
Chapter Summary.
Review Questions.
Exercises.
19 Data Warehouse Deployment.
Chapter Objectives.
Data Warehouse Testing.
Major Deployment Activities.
Considerations for a Pilot.
Security.
Backup and Recovery.
Chapter Summary.
Review Questions.
Exercises.
20 Growth and Maintenance.
Chapter Objectives.
Monitoring the Data Warehouse.
User Training and Support.
Managing the Data Warehouse.
Chapter Summary.
Review Questions.
Exercises.
Answers to Selected Exercises.
Appendix A. Project Life Cycl e Steps and Checklists.
Appendix B. Critical Factors for Success.
Appendix C. Guidelines for Evaluating Vendor Solutions.
Appendix D. Highlights of Vendors and Products.
Appendix E. Real–World Examples of Best Practices .
References.
Glossary.
Index.

Okładka tylna:

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 se veral 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.