Data virtualization for business intelligence architectures : revolutionizing data integration for data warehouses / Rick F. van der Lans.

Includes bibliographical references and index.

Front Cover; Data Virtualization for Business Intelligence Systems; Copyright Page; Contents; Foreword; Preface; Introduction; Who Should Read This Book?; Prerequisite Knowledge; Terms and Definitions; And Finally ... ; About the Author; 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; 1.5.1 Data Virtualization versus Encapsulation and Information Hiding; 1.5.2 Data Virtualization versus Abstraction.

1.5.3 Data Virtualization versus Data Federation1.5.4 Data Virtualization versus Data Integration; 1.5.5 Data Virtualization versus Enterprise Information Integration; 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 Book2 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; 2.5.1 The Data Warehouse; 2.5.2 The Data Marts; 2.5.3 The Data Staging Area; 2.5.4 The Operational Data Store; 2.5.5 The Personal Data Stores; 2.5.6 A Comparison of the Different Types of Data Stores; 2.6 Normalized Schemas, Star Schemas, and Snowflake Schemas; 2.6.1 Normalized Schemas; 2.6.2 Denormalized Schemas; 2.6.3 Star Schemas.

2.6.4 Snowflake Schemas2.7 Data Transformation with Extract Transform Load, Extract Load Transform, and Replication; 2.7.1 Extract Transform Load; 2.7.2 Extract Load Transform; 2.7.3 Replication; 2.8 Overview of Business Intelligence Architectures; 2.9 New Forms of Reporting and Analytics; 2.9.1 Operational Reporting and Analytics; 2.9.2 Deep and Big Data Analytics; 2.9.3 Self-Service Reporting and Analytics; 2.9.4 Unrestricted Ad-Hoc Analysis; 2.9.5 360-Degree Reporting; 2.9.6 Exploratory Analysis; 2.9.7 Text-Based Analysis; 2.10 Disadvantages of Classic Business Intelligence Systems.

2.11 Summary3 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; 3.8.1 XML and JSON Documents; 3.8.2 Web Services; 3.8.3 Spreadsheets; 3.8.4 NoSQL Databases; 3.8.5 Multidimensional Cubes and MDX; 3.8.6 Semistructured Data; 3.8.7 Unstructured Data.

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 re.

English.

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