http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Data virtualization for business intelligence systems
revolutionizing data integration for data warehouses
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.
1.15. Structure of This Book -- 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 -- 2.5.1. 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.
1.5.3. Data Virtualization versus Data Federation -- 1.5.4. 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.
2.11. Summary -- 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 -- 3.8.1. 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.
2.6.4. Snowflake Schemas -- 2.7. 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.
Includes bibliographical references and index.