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| Foreword | |
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| Preface | |
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| About the Author | |
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| Introduction to Data Virtualization | |
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| Introduction | |
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| The World of Business Intelligence Is Changing | |
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| Introduction to Virtualization | |
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| What Is Data Virtualization? | |
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| Data Virtualization and Related Concepts | |
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| Data Virtualization versus Encapsulation and Information Hiding | |
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| Data Virtualization versus Abstraction | |
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| Data Virtualization versus Data Federation | |
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| Data Virtualization versus Data Integration | |
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| Data Virtualization versus Enterprise Information Integration | |
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| Definition of Data Virtualization | |
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| Technical Advantages of Data Virtualization | |
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| Different Implementations of Data Virtualization | |
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| Overview of Data Virtualization Servers | |
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| Open versus Closed Data Virtualization Servers | |
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| Other Forms of Data Integration | |
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| The Modules of a Data Virtualization Server | |
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| The History of Data Virtualization | |
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| The Sample Database: World Class Movies | |
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| Structure of This Book | |
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| Business Intelligence and Data Warehousing | |
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| Introduction | |
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| What Is Business Intelligence? | |
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| Management Levels and Decision Making | |
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| Business Intelligence Systems | |
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| The Data Stores of a Business Intelligence System | |
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| The Data Warehouse | |
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| The Data Marts | |
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| The Data Staging Area | |
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| The Operational Data Store | |
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| The Personal Data Stores | |
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| A Comparison of the Different Types of Data Stores | |
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| Normalized Schemas, Star Schemas, and Snowflake Schemas | |
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| Normalized Schemas | |
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| Denormalized Schemas | |
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| Star Schemas | |
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| Snowflake Schemas | |
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| Data Transformation with Extract Transform Load, Extract Load Transform, and Replication | |
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| Extract Transform Load | |
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| Extract Load Transform | |
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| Replication | |
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| Overview of Business Intelligence Architectures | |
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| New Forms of Reporting and Analytics | |
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| Operational Reporting and Analytics | |
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| Deep and Big Data Analytics | |
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| Self-Service Reporting and Analytics | |
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| Unrestricted Ad-Hoc Analysis | |
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| 360-Degree Reporting | |
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| Exploratory Analysis | |
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| Text-Based Analysis | |
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| Disadvantages of Classic Business Intelligence Systems | |
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| Summary | |
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| Data Virtualization Server: The Building Blocks | |
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| Introduction | |
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| The High-Level Architecture of a Data Virtualization Server | |
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| Importing Source Tables and Defining Wrappers | |
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| Defining Virtual Tables and Mappings | |
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| Examples of Virtual Tables and Mappings | |
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| Virtual Tables and Data Modeling | |
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| Nesting Virtual Tables and Shared Specifications | |
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| Importing Nonrelational Data | |
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| XML and JSON Documents | |
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| Web Services | |
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| Spreadsheets | |
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| NoSQL Databases | |
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| Multidimensional Cubes and MDX | |
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| Semistructured Data | |
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| Unstructured Data | |
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| Publishing Virtual Tables | |
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| The Internal Data Model | |
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| Updatable Virtual Tables and Transaction Management | |
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| Data Virtualization Server: Management and Security | |
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| Introduction | |
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| Impact and Lineage Analysis | |
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| Synchronization of Source Tables, Wrapper Tables, and Virtual Tables | |
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| Security of Data: Authentication and Authorization | |
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| Monitoring, Management, and Administration | |
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| Data Virtualization Server: Caching of Virtual Tables | |
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| Introduction | |
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| The Cache of a Virtual Table | |
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| When to Use Caching | |
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| Caches versus Data Marts | |
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| Where Is the Cache Kept? | |
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| Refreshing Caches | |
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| Full Refreshing, Incremental Refreshing, and Live Refreshing | |
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| Online Refreshing and Offline Refreshing | |
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| Cache Replication | |
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| Data Virtualization Server: Query Optimization Techniques | |
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| Introduction | |
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| A Refresher Course on Query Optimization | |
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| The Ten Stages of Query Processing by a Data Virtualization Server | |
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| The Intelligence Level of the Data Stores | |
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| Optimization through Query Substitution | |
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| Optimization through Pushdown | |
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| Optimization through Query Expansion (Query Injection) | |
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| Optimization through Ship Joins | |
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| Optimization through Sort-Merge Joins | |
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| Optimization by Caching | |
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| Optimization and Statistical Data | |
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| Optimization through Hints | |
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| Optimization through SQL Override | |
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| Explaining the Processing Strategy | |
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| Deploying Data Virtualization in Business Intelligence Systems | |
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| Introduction | |
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| A Business Intelligence System Based on Data Virtualization | |
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| Advantages of Deploying Data Virtualization | |
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| Disadvantages of Deploying Data Virtualization | |
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| Strategies for Adopting Data Virtualization | |
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| Strategy 1: Introducing Data Virtualization in an Existing Business Intelligence System | |
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| Strategy 2: Developing a New Business Intelligence System with Data Virtualization | |
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| Strategy 3: Developing a New Business Intelligence System Combining Source and Transformed Data | |
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| Application Areas of Data Virtualization | |
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| Unified Data Access | |
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| Virtual Data Mart | |
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| Virtual Data Warehouse-Based on Data Marts | |
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| Virtual Data Warehouse-Based on Production Databases | |
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| Extended Data Warehouse | |
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| Operational Reporting and Analytics | |
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| Operational Data Warehouse | |
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| Virtual Corporate Data Warehouse | |
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| Self-Service Reporting and Analytics | |
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| Virtual Sandbox | |
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| Prototyping | |
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| Analyzing Semistructured and Unstructured Data | |
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| Disposable Reports | |
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| Extending Business Intelligence Systems with External Users | |
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| Myths on Data Virtualization | |
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| Design Guidelines for Data Virtualization | |
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| Introduction | |
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| Incorrect Data and Data Quality | |
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| Different Forms of Incorrect Data | |
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| Integrity Rules and Incorrect Data | |
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| Filtering, Flagging, and Restoring Incorrect Data | |
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| Examples of Filtering Incorrect Data | |
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| Examples of Flagging Incorrect Data | |
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| Examples of Restoring Misspelled Data | |
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| Complex and Irregular Data Structures | |
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| Codes without Names | |
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| Inconsistent Key Values | |
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| Repeating Groups | |
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| Recursive Data Structures | |
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| Implementing Transformations in Wrappers or Mappings | |
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| Analyzing Incorrect Data | |
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| Different Users and Different Definitions | |
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| Time Inconsistency of Data | |
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| Data Stores and Data Transmission | |
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| Retrieving Data from Production Systems | |
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| Joining Historical and Operational Data | |
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| Dealing with Organizational Changes | |
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| Archiving Data | |
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| Data Virtualization and Service-Oriented Architecture | |
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| Introduction | |
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| Service-Oriented Architectures in a Nutshell | |
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| Basic Services, Composite Services, Business Process Services, and Data Services | |
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| Developing Data Services with a Data Virtualization Server | |
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| Developing Composite Services with a Data Virtualization Server | |
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| Services and the Internal Data Model | |
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| Data Virtualization and Master Data Management | |
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| Introduction | |
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| Data Is a Critical Asset for Every Organization | |
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| The Need for a 360-Degree View of Business Objects | |
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| What Is Master Data? | |
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| What Is Master Data Management? | |
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| A Master Data Management System | |
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| Master Data Management for Integrating Data | |
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| Integrating Master Data Management and Data Virtualization | |
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| Data Virtualization, Information Management, and Data Governance | |
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| Introduction | |
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| Impact of Data Virtualization on Information Modeling and Database Design | |
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| Impact of Data Virtualization on Data Profiling | |
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| Impact of Data Virtualization on Data Cleansing | |
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| Impact of Data Virtualization on Data Governance | |
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| The Data Delivery Platform-A New Architecture for Business Intelligence Systems | |
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| Introduction | |
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| The Data Delivery Platform in a Nutshell | |
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| The Definition of the Data Delivery Platform | |
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| The Data Delivery Platform and Other Business Intelligence Architectures | |
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| The Requirements of the Data Delivery Platform | |
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| The Data Delivery Platform versus Data Virtualization | |
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| Explanation of the Name | |
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| A Personal Note | |
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| The Future of Data Virtualization | |
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| Introduction | |
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| The Future of Data Virtualization According to Rick F. van der Lans | |
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| New and Enhanced Query Optimization Techniques | |
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| Exploiting New Hardware Technology | |
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| Extending the Design Module | |
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| Data Quality Features | |
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| Support for the Push-Model for Data Access | |
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| Blending of Data Virtualization, Extract Transform Load, Extract Load Transform, and Replication | |
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| The Future of Data Virtualization According to David Besemer, CTO of Composite Software | |
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| The Empowered Consumer Gains Ubiquitous Data Access | |
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| IT's Back Office Becomes the Cloud | |
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| Data Virtualization of the Future Is a Global Data Fabric | |
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| Conclusion | |
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| The Future of Data Virtualization According to Alberto Pan, CTO of Denodo Technologies | |
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| The Future of Data Virtualization According to James Markarian, CTO of Informatica Corporation | |
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| How to Maximize Return on Data with Data Virtualization | |
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| Beyond Looking Under the Hood | |
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| Bibliography | |
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| Index | |