Data Virtualization for Business Intelligence Systems : Revolutionizing Data Integration for Data Warehouses

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  • Format: Paperback
  • Copyright: 2012-07-25
  • Publisher: Elsevier Science Ltd

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Data virtualization can help you accomplish your goals with more flexibility and agility. Learn what it is and how and why it should be used with Data Virtualization for Business Intelligence Systems. In this book, expert author Rick van der Lans explains how data virtualization servers work, what techniques to use to optimize access to various data sources and how these products can be applied in different projects. You'll learn the difference is between this new form of data integration and older forms, such as ETL and replication, and gain a clear understanding of how data virtualization really works. Data Virtualization for Business Intelligence Systems outlines the advantages and disadvantages of data virtualization and illustrates how data virtualization should be applied in data warehouse environments. You'll come away with a comprehensive understanding of how data virtualization will make data warehouse environments more flexible and how it make developing operational BI applications easier. Van der Lans also describes the relationship between data virtualization and related topics, such as master data management, governance, and information management, so you come away with a big-picture understanding as well as all the practical know-how you need to virtualize your data. First independent book on data virtualization that explains in a product-independent way how data virtualization technology works. Illustrates concepts using examples developed with commercially available products. Shows you how to solve common data integration challenges such as data quality, system interference, and overall performance by following practical guidelines on using data virtualization. Apply data virtualization right away with three chapters full of practical implementation guidance. Understand the big picture of data virtualization and its relationship with data governance and information management.

Author Biography

Rick van der Lans Independent BI analyst Managing Director R20/Consultancy B.V.

Table of Contents

Forewordp. xiii
Prefacep. xv
About the Authorp. xix
Introduction to Data Virtualizationp. 1
Introductionp. 1
The World of Business Intelligence Is Changingp. 1
Introduction to Virtualizationp. 3
What Is Data Virtualization?p. 4
Data Virtualization and Related Conceptsp. 5
Data Virtualization versus Encapsulation and Information Hidingp. 5
Data Virtualization versus Abstractionp. 6
Data Virtualization versus Data Federationp. 7
Data Virtualization versus Data Integrationp. 8
Data Virtualization versus Enterprise Information Integrationp. 9
Definition of Data Virtualizationp. 9
Technical Advantages of Data Virtualizationp. 10
Different Implementations of Data Virtualizationp. 14
Overview of Data Virtualization Serversp. 14
Open versus Closed Data Virtualization Serversp. 15
Other Forms of Data Integrationp. 16
The Modules of a Data Virtualization Serverp. 18
The History of Data Virtualizationp. 19
The Sample Database: World Class Moviesp. 22
Structure of This Bookp. 25
Business Intelligence and Data Warehousingp. 27
Introductionp. 27
What Is Business Intelligence?p. 27
Management Levels and Decision Makingp. 28
Business Intelligence Systemsp. 29
The Data Stores of a Business Intelligence Systemp. 30
The Data Warehousep. 30
The Data Martsp. 34
The Data Staging Areap. 35
The Operational Data Storep. 37
The Personal Data Storesp. 38
A Comparison of the Different Types of Data Storesp. 38
Normalized Schemas, Star Schemas, and Snowflake Schemasp. 39
Normalized Schemasp. 40
Denormalized Schemasp. 40
Star Schemasp. 41
Snowflake Schemasp. 43
Data Transformation with Extract Transform Load, Extract Load Transform, and Replicationp. 44
Extract Transform Loadp. 44
Extract Load Transformp. 45
Replicationp. 46
Overview of Business Intelligence Architecturesp. 47
New Forms of Reporting and Analyticsp. 48
Operational Reporting and Analyticsp. 48
Deep and Big Data Analyticsp. 49
Self-Service Reporting and Analyticsp. 49
Unrestricted Ad-Hoc Analysisp. 50
360-Degree Reportingp. 51
Exploratory Analysisp. 51
Text-Based Analysisp. 52
Disadvantages of Classic Business Intelligence Systemsp. 53
Summaryp. 56
Data Virtualization Server: The Building Blocksp. 59
Introductionp. 59
The High-Level Architecture of a Data Virtualization Serverp. 59
Importing Source Tables and Defining Wrappersp. 60
Defining Virtual Tables and Mappingsp. 62
Examples of Virtual Tables and Mappingsp. 66
Virtual Tables and Data Modelingp. 76
Nesting Virtual Tables and Shared Specificationsp. 77
Importing Nonrelational Datap. 79
XML and JSON Documentsp. 79
Web Servicesp. 84
Spreadsheetsp. 86
NoSQL Databasesp. 86
Multidimensional Cubes and MDXp. 89
Semistructured Datap. 92
Unstructured Datap. 95
Publishing Virtual Tablesp. 96
The Internal Data Modelp. 101
Updatable Virtual Tables and Transaction Managementp. 106
Data Virtualization Server: Management and Securityp. 109
Introductionp. 109
Impact and Lineage Analysisp. 109
Synchronization of Source Tables, Wrapper Tables, and Virtual Tablesp. 110
Security of Data: Authentication and Authorizationp. 112
Monitoring, Management, and Administrationp. 114
Data Virtualization Server: Caching of Virtual Tablesp. 119
Introductionp. 119
The Cache of a Virtual Tablep. 119
When to Use Cachingp. 120
Caches versus Data Martsp. 122
Where Is the Cache Kept?p. 122
Refreshing Cachesp. 123
Full Refreshing, Incremental Refreshing, and Live Refreshingp. 124
Online Refreshing and Offline Refreshingp. 125
Cache Replicationp. 126
Data Virtualization Server: Query Optimization Techniquesp. 127
Introductionp. 127
A Refresher Course on Query Optimizationp. 128
The Ten Stages of Query Processing by a Data Virtualization Serverp. 132
The Intelligence Level of the Data Storesp. 134
Optimization through Query Substitutionp. 134
Optimization through Pushdownp. 137
Optimization through Query Expansion (Query Injection)p. 139
Optimization through Ship Joinsp. 140
Optimization through Sort-Merge Joinsp. 141
Optimization by Cachingp. 142
Optimization and Statistical Datap. 142
Optimization through Hintsp. 143
Optimization through SQL Overridep. 143
Explaining the Processing Strategyp. 145
Deploying Data Virtualization in Business Intelligence Systemsp. 147
Introductionp. 147
A Business Intelligence System Based on Data Virtualizationp. 147
Advantages of Deploying Data Virtualizationp. 148
Disadvantages of Deploying Data Virtualizationp. 151
Strategies for Adopting Data Virtualizationp. 151
Strategy 1: Introducing Data Virtualization in an Existing Business Intelligence Systemp. 152
Strategy 2: Developing a New Business Intelligence System with Data Virtualizationp. 157
Strategy 3: Developing a New Business Intelligence System Combining Source and Transformed Datap. 161
Application Areas of Data Virtualizationp. 163
Unified Data Accessp. 163
Virtual Data Martp. 163
Virtual Data Warehouse-Based on Data Martsp. 165
Virtual Data Warehouse-Based on Production Databasesp. 165
Extended Data Warehousep. 167
Operational Reporting and Analyticsp. 167
Operational Data Warehousep. 168
Virtual Corporate Data Warehousep. 169
Self-Service Reporting and Analyticsp. 170
Virtual Sandboxp. 171
Prototypingp. 171
Analyzing Semistructured and Unstructured Datap. 172
Disposable Reportsp. 173
Extending Business Intelligence Systems with External Usersp. 173
Myths on Data Virtualizationp. 174
Design Guidelines for Data Virtualizationp. 177
Introductionp. 177
Incorrect Data and Data Qualityp. 177
Different Forms of Incorrect Datap. 178
Integrity Rules and Incorrect Datap. 179
Filtering, Flagging, and Restoring Incorrect Datap. 179
Examples of Filtering Incorrect Datap. 180
Examples of Flagging Incorrect Datap. 184
Examples of Restoring Misspelled Datap. 186
Complex and Irregular Data Structuresp. 188
Codes without Namesp. 188
Inconsistent Key Valuesp. 190
Repeating Groupsp. 192
Recursive Data Structuresp. 192
Implementing Transformations in Wrappers or Mappingsp. 197
Analyzing Incorrect Datap. 197
Different Users and Different Definitionsp. 198
Time Inconsistency of Datap. 199
Data Stores and Data Transmissionp. 200
Retrieving Data from Production Systemsp. 202
Joining Historical and Operational Datap. 203
Dealing with Organizational Changesp. 204
Archiving Datap. 205
Data Virtualization and Service-Oriented Architecturep. 207
Introductionp. 207
Service-Oriented Architectures in a Nutshellp. 207
Basic Services, Composite Services, Business Process Services, and Data Servicesp. 209
Developing Data Services with a Data Virtualization Serverp. 211
Developing Composite Services with a Data Virtualization Serverp. 213
Services and the Internal Data Modelp. 215
Data Virtualization and Master Data Managementp. 217
Introductionp. 217
Data Is a Critical Asset for Every Organizationp. 217
The Need for a 360-Degree View of Business Objectsp. 219
What Is Master Data?p. 219
What Is Master Data Management?p. 221
A Master Data Management Systemp. 222
Master Data Management for Integrating Datap. 224
Integrating Master Data Management and Data Virtualizationp. 224
Data Virtualization, Information Management, and Data Governancep. 231
Introductionp. 231
Impact of Data Virtualization on Information Modeling and Database Designp. 231
Impact of Data Virtualization on Data Profilingp. 234
Impact of Data Virtualization on Data Cleansingp. 239
Impact of Data Virtualization on Data Governancep. 239
The Data Delivery Platform-A New Architecture for Business Intelligence Systemsp. 243
Introductionp. 243
The Data Delivery Platform in a Nutshellp. 243
The Definition of the Data Delivery Platformp. 244
The Data Delivery Platform and Other Business Intelligence Architecturesp. 245
The Requirements of the Data Delivery Platformp. 247
The Data Delivery Platform versus Data Virtualizationp. 249
Explanation of the Namep. 250
A Personal Notep. 251
The Future of Data Virtualizationp. 253
Introductionp. 253
The Future of Data Virtualization According to Rick F. van der Lansp. 254
New and Enhanced Query Optimization Techniquesp. 254
Exploiting New Hardware Technologyp. 255
Extending the Design Modulep. 256
Data Quality Featuresp. 258
Support for the Push-Model for Data Accessp. 258
Blending of Data Virtualization, Extract Transform Load, Extract Load Transform, and Replicationp. 259
The Future of Data Virtualization According to David Besemer, CTO of Composite Softwarep. 260
The Empowered Consumer Gains Ubiquitous Data Accessp. 261
IT's Back Office Becomes the Cloudp. 261
Data Virtualization of the Future Is a Global Data Fabricp. 261
Conclusionp. 262
The Future of Data Virtualization According to Alberto Pan, CTO of Denodo Technologiesp. 262
The Future of Data Virtualization According to James Markarian, CTO of Informatica Corporationp. 264
How to Maximize Return on Data with Data Virtualizationp. 265
Beyond Looking Under the Hoodp. 266
Bibliographyp. 267
Indexp. 269
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