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Index
Cover image
Title page
Table of Contents
Copyright
Dedication
Foreword
Preface
About the Author
Chapter 1. Introduction to Data Virtualization
1.1 Introduction
1.2 The World of Business Intelligence Is Changing
1.3 Introduction to Virtualization
1.4 What Is Data Virtualization?
1.5 Data Virtualization and Related Concepts
1.6 Definition of Data Virtualization
1.7 Technical Advantages of Data Virtualization
1.8 Different Implementations of Data Virtualization
1.9 Overview of Data Virtualization Servers
1.10 Open versus Closed Data Virtualization Servers
1.11 Other Forms of Data Integration
1.12 The Modules of a Data Virtualization Server
1.13 The History of Data Virtualization
1.14 The Sample Database: World Class Movies
1.15 Structure of This Book
Chapter 2. Business Intelligence and Data Warehousing
2.1 Introduction
2.2 What Is Business Intelligence?
2.3 Management Levels and Decision Making
2.4 Business Intelligence Systems
2.5 The Data Stores of a Business Intelligence System
2.6 Normalized Schemas, Star Schemas, and Snowflake Schemas
2.7 Data Transformation with Extract Transform Load, Extract Load Transform, and Replication
2.8 Overview of Business Intelligence Architectures
2.9 New Forms of Reporting and Analytics
2.10 Disadvantages of Classic Business Intelligence Systems
2.11 Summary
Chapter 3. Data Virtualization Server: The Building Blocks
3.1 Introduction
3.2 The High-Level Architecture of a Data Virtualization Server
3.3 Importing Source Tables and Defining Wrappers
3.4 Defining Virtual Tables and Mappings
3.5 Examples of Virtual Tables and Mappings
3.6 Virtual Tables and Data Modeling
3.7 Nesting Virtual Tables and Shared Specifications
3.8 Importing Nonrelational Data
3.9 Publishing Virtual Tables
3.10 The Internal Data Model
3.11 Updatable Virtual Tables and Transaction Management
Chapter 4. Data Virtualization Server: Management and Security
4.1 Introduction
4.2 Impact and Lineage Analysis
4.3 Synchronization of Source Tables, Wrapper Tables, and Virtual Tables
4.4 Security of Data: Authentication and Authorization
4.5 Monitoring, Management, and Administration
Chapter 5. Data Virtualization Server: Caching of Virtual Tables
5.1 Introduction
5.2 The Cache of a Virtual Table
5.3 When to Use Caching
5.4 Caches versus Data Marts
5.5 Where Is the Cache Kept?
5.6 Refreshing Caches
5.7 Full Refreshing, Incremental Refreshing, and Live Refreshing
5.8 Online Refreshing and Offline Refreshing
5.9 Cache Replication
Chapter 6. Data Virtualization Server: Query Optimization Techniques
6.1 Introduction
6.2 A Refresher Course on Query Optimization
6.3 The Ten Stages of Query Processing by a Data Virtualization Server
6.4 The Intelligence Level of the Data Stores
6.5 Optimization through Query Substitution
6.6 Optimization through Pushdown
6.7 Optimization through Query Expansion (Query Injection)
6.8 Optimization through Ship Joins
6.9 Optimization through Sort-Merge Joins
6.10 Optimization by Caching
6.11 Optimization and Statistical Data
6.12 Optimization through Hints
6.13 Optimization through SQL Override
6.14 Explaining the Processing Strategy
Chapter 7. Deploying Data Virtualization in Business Intelligence Systems
7.1 Introduction
7.2 A Business Intelligence System Based on Data Virtualization
7.3 Advantages of Deploying Data Virtualization
7.4 Disadvantages of Deploying Data Virtualization
7.5 Strategies for Adopting Data Virtualization
7.6 Application Areas of Data Virtualization
7.7 Myths on Data Virtualization
Chapter 8. Design Guidelines for Data Virtualization
8.1 Introduction
8.2 Incorrect Data and Data Quality
8.3 Complex and Irregular Data Structures
8.4 Implementing Transformations in Wrappers or Mappings
8.5 Analyzing Incorrect Data
8.6 Different Users and Different Definitions
8.7 Time Inconsistency of Data
8.8 Data Stores and Data Transmission
8.9 Retrieving Data from Production Systems
8.10 Joining Historical and Operational Data
8.11 Dealing with Organizational Changes
8.12 Archiving Data
Chapter 9. Data Virtualization and Service-Oriented Architecture
9.1 Introduction
9.2 Service-Oriented Architectures in a Nutshell
9.3 Basic Services, Composite Services, Business Process Services, and Data Services
9.4 Developing Data Services with a Data Virtualization Server
9.5 Developing Composite Services with a Data Virtualization Server
9.6 Services and the Internal Data Model
Chapter 10. Data Virtualization and Master Data Management
10.1 Introduction
10.2 Data Is a Critical Asset for Every Organization
10.3 The Need for a 360-Degree View of Business Objects
10.4 What Is Master Data?
10.5 What Is Master Data Management?
10.6 A Master Data Management System
10.7 Master Data Management for Integrating Data
10.8 Integrating Master Data Management and Data Virtualization
Chapter 11. Data Virtualization, Information Management, and Data Governance
11.1 Introduction
11.2 Impact of Data Virtualization on Information Modeling and Database Design
11.3 Impact of Data Virtualization on Data Profiling
11.4 Impact of Data Virtualization on Data Cleansing
11.5 Impact of Data Virtualization on Data Governance
Chapter 12. The Data Delivery Platform—A New Architecture for Business Intelligence Systems
12.1 Introduction
12.2 The Data Delivery Platform in a Nutshell
12.3 The Definition of the Data Delivery Platform
12.4 The Data Delivery Platform and Other Business Intelligence Architectures
12.5 The Requirements of the Data Delivery Platform
12.6 The Data Delivery Platform versus Data Virtualization
12.7 Explanation of the Name
12.8 A Personal Note
Chapter 13. The Future of Data Virtualization
13.1 Introduction
13.2 The Future of Data Virtualization According to Rick F. van der Lans
13.3 The Future of Data Virtualization According to David Besemer, CTO of Composite Software
13.4 The Future of Data Virtualization According to Alberto Pan, CTO of Denodo Technologies
13.5 The Future of Data Virtualization According to James Markarian, CTO of Informatica Corporation
Bibliography
Index
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