Data virtualization for business intelligence systems : revolutionizing data integration for data warehouses /

Mostrar otras versiones (1)

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 t...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Lans, Rick F. van der
Formato: Electrónico eBook
Idioma:Inglés
Publicado: [Place of publication not identified] : Morgan Kaufmann, 2012.
Temas:
Data warehousing.
Management information systems.
Virtual computer systems.
Business intelligence.
Entrep�ots de donn�ees (Informatique)
Syst�emes d'information de gestion.
Syst�emes virtuels (Informatique)
Data-Warehouse-Konzept
Managementinformationssystem
Business Intelligence
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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.
  • 3.9. Publishing Virtual Tables.

Ejemplares similares