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Data Virtualization for Business Intelligence Systems: Revolutionizing Data Integration for Data Warehouses [Pehme köide]

3.80/5 (30 hinnangut Goodreads-ist)
(Managing Director, R20/Consultancy)
  • Formaat: Paperback / softback, 296 pages, kõrgus x laius: 235x191 mm, kaal: 620 g, Contains 1 Digital (delivered electronically)
  • Ilmumisaeg: 30-Aug-2012
  • Kirjastus: Morgan Kaufmann Publishers In
  • ISBN-10: 0123944252
  • ISBN-13: 9780123944252
Teised raamatud teemal:
Data Virtualization for Business Intelligence Systems: Revolutionizing Data Integration for Data WarehousesSuurem pilt
  • Formaat: Paperback / softback, 296 pages, kõrgus x laius: 235x191 mm, kaal: 620 g, Contains 1 Digital (delivered electronically)
  • Ilmumisaeg: 30-Aug-2012
  • Kirjastus: Morgan Kaufmann Publishers In
  • ISBN-10: 0123944252
  • ISBN-13: 9780123944252
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780123944252.html
Märksõnad:
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. Youll 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. Youll 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.

Arvustused

"This book for those in business and information management explains how to use data virtualization in business intelligence systemsSome general knowledge of data warehousing, business intelligence, and database technology is assumed." --Reference and Research Book News, December 2012

Muu info

The practical guide for understanding and deploying data virtualization in data warehouse environments.
Foreword xiii
Preface xv
About the Author xix
Chapter 1 Introduction to Data Virtualization
1(26)
1.1 Introduction
1(1)
1.2 The World of Business Intelligence Is Changing
1(2)
1.3 Introduction to Virtualization
3(1)
1.4 What Is Data Virtualization?
4(1)
1.5 Data Virtualization and Related Concepts
5(4)
1.5.1 Data Virtualization versus Encapsulation and Information Hiding
5(1)
1.5.2 Data Virtualization versus Abstraction
6(1)
1.5.3 Data Virtualization versus Data Federation
7(1)
1.5.4 Data Virtualization versus Data Integration
8(1)
1.5.5 Data Virtualization versus Enterprise Information Integration
9(1)
1.6 Definition of Data Virtualization
9(1)
1.7 Technical Advantages of Data Virtualization
10(4)
1.8 Different Implementations of Data Virtualization
14(1)
1.9 Overview of Data Virtualization Servers
14(1)
1.10 Open versus Closed Data Virtualization Servers
15(1)
1.11 Other Forms of Data Integration
16(2)
1.12 The Modules of a Data Virtualization Server
18(1)
1.13 The History of Data Virtualization
19(3)
1.14 The Sample Database: World Class Movies
22(3)
1.15 Structure of This Book
25(2)
Chapter 2 Business Intelligence and Data Warehousing
27(32)
2.1 Introduction
27(1)
2.2 What Is Business Intelligence?
27(1)
2.3 Management Levels and Decision Making
28(1)
2.4 Business Intelligence Systems
29(1)
2.5 The Data Stores of a Business Intelligence System
30(9)
2.5.1 The Data Warehouse
30(4)
2.5.2 The Data Marts
34(1)
2.5.3 The Data Staging Area
35(2)
2.5.4 The Operational Data Store
37(1)
2.5.5 The Personal Data Stores
38(1)
2.5.6 A Comparison of the Different Types of Data Stores
38(1)
2.6 Normalized Schemas, Star Schemas, and Snowflake Schemas
39(5)
2.6.1 Normalized Schemas
40(1)
2.6.2 Denormalized Schemas
40(1)
2.6.3 Star Schemas
41(2)
2.6.4 Snowflake Schemas
43(1)
2.7 Data Transformation with Extract Transform Load, Extract Load Transform, and Replication
44(3)
2.7.1 Extract Transform Load
44(1)
2.7.2 Extract Load Transform
45(1)
2.7.3 Replication
46(1)
2.8 Overview of Business Intelligence Architectures
47(1)
2.9 New Forms of Reporting and Analytics
48(5)
2.9.1 Operational Reporting and Analytics
48(1)
2.9.2 Deep and Big Data Analytics
49(1)
2.9.3 Self-Service Reporting and Analytics
49(1)
2.9.4 Unrestricted Ad-Hoc Analysis
50(1)
2.9.5 360-Degree Reporting
51(1)
2.9.6 Exploratory Analysis
51(1)
2.9.7 Text-Based Analysis
52(1)
2.10 Disadvantages of Classic Business Intelligence Systems
53(3)
2.11 Summary
56(3)
Chapter 3 Data Virtualization Server: The Building Blocks
59(50)
3.1 Introduction
59(1)
3.2 The High-Level Architecture of a Data Virtualization Server
59(1)
3.3 Importing Source Tables and Defining Wrappers
60(2)
3.4 Defining Virtual Tables and Mappings
62(4)
3.5 Examples of Virtual Tables and Mappings
66(10)
3.6 Virtual Tables and Data Modeling
76(1)
3.7 Nesting Virtual Tables and Shared Specifications
77(2)
3.8 Importing Nonrelational Data
79(17)
3.8.1 XML and JSON Documents
79(5)
3.8.2 Web Services
84(2)
3.8.3 Spreadsheets
86(1)
3.8.4 NoSQL Databases
86(3)
3.8.5 Multidimensional Cubes and MDX
89(3)
3.8.6 Semistructured Data
92(3)
3.8.7 Unstructured Data
95(1)
3.9 Publishing Virtual Tables
96(5)
3.10 The Internal Data Model
101(5)
3.11 Updatable Virtual Tables and Transaction Management
106(3)
Chapter 4 Data Virtualization Server: Management and Security
109(10)
4.1 Introduction
109(1)
4.2 Impact and Lineage Analysis
109(1)
4.3 Synchronization of Source Tables, Wrapper Tables, and Virtual Tables
110(2)
4.4 Security of Data: Authentication and Authorization
112(2)
4.5 Monitoring, Management, and Administration
114(5)
Chapter 5 Data Virtualization Server: Caching of Virtual Tables
119(8)
5.1 Introduction
119(1)
5.2 The Cache of a Virtual Table
119(1)
5.3 When to Use Caching
120(2)
5.4 Caches versus Data Marts
122(1)
5.5 Where Is the Cache Kept?
122(1)
5.6 Refreshing Caches
123(1)
5.7 Full Refreshing, Incremental Refreshing, and Live Refreshing
124(1)
5.8 Online Refreshing and Offline Refreshing
125(1)
5.9 Cache Replication
126(1)
Chapter 6 Data Virtualization Server: Query Optimization Techniques
127(20)
6.1 Introduction
127(1)
6.2 A Refresher Course on Query Optimization
128(4)
6.3 The Ten Stages of Query Processing by a Data Virtualization Server
132(2)
6.4 The Intelligence Level of the Data Stores
134(1)
6.5 Optimization through Query Substitution
134(3)
6.6 Optimization through Pushdown
137(2)
6.7 Optimization through Query Expansion (Query Injection)
139(1)
6.8 Optimization through Ship Joins
140(1)
6.9 Optimization through Sort-Merge Joins
141(1)
6.10 Optimization by Caching
142(1)
6.11 Optimization and Statistical Data
142(1)
6.12 Optimization through Hints
143(1)
6.13 Optimization through SQL Override
143(2)
6.14 Explaining the Processing Strategy
145(2)
Chapter 7 Deploying Data Virtualization in Business Intelligence Systems
147(30)
7.1 Introduction
147(1)
7.2 A Business Intelligence System Based on Data Virtualization
147(1)
7.3 Advantages of Deploying Data Virtualization
148(3)
7.4 Disadvantages of Deploying Data Virtualization
151(1)
7.5 Strategies for Adopting Data Virtualization
151(12)
7.5.1 Strategy 1: Introducing Data Virtualization in an Existing Business Intelligence System
152(5)
7.5.2 Strategy 2: Developing a New Business Intelligence System with Data Virtualization
157(4)
7.5.3 Strategy 3: Developing a New Business Intelligence System Combining Source and Transformed Data
161(2)
7.6 Application Areas of Data Virtualization
163(11)
7.6.1 Unified Data Access
163(1)
7.6.2 Virtual Data Mart
163(2)
7.6.3 Virtual Data Warehouse---Based on Data Marts
165(1)
7.6.4 Virtual Data Warehouse---Based on Production Databases
165(2)
7.6.5 Extended Data Warehouse
167(1)
7.6.6 Operational Reporting and Analytics
167(1)
7.6.7 Operational Data Warehouse
168(1)
7.6.8 Virtual Corporate Data Warehouse
169(1)
7.6.9 Self-Service Reporting and Analytics
170(1)
7.6.10 Virtual Sandbox
171(1)
7.6.11 Prototyping
171(1)
7.6.12 Analyzing Semistructured and Unstructured Data
172(1)
7.6.13 Disposable Reports
173(1)
7.6.14 Extending Business Intelligence Systems with External Users
173(1)
7.7 Myths on Data Virtualization
174(3)
Chapter 8 Design Guidelines for Data Virtualization
177(30)
8.1 Introduction
177(1)
8.2 Incorrect Data and Data Quality
177(11)
8.2.1 Different Forms of Incorrect Data
178(1)
8.2.2 Integrity Rules and Incorrect Data
179(1)
8.2.3 Filtering, Flagging, and Restoring Incorrect Data
179(1)
8.2.4 Examples of Filtering Incorrect Data
180(4)
8.2.5 Examples of Flagging Incorrect Data
184(2)
8.2.6 Examples of Restoring Misspelled Data
186(2)
8.3 Complex and Irregular Data Structures
188(9)
8.3.1 Codes without Names
188(2)
8.3.2 Inconsistent Key Values
190(2)
8.3.3 Repeating Groups
192(1)
8.3.4 Recursive Data Structures
192(5)
8.4 Implementing Transformations in Wrappers or Mappings
197(1)
8.5 Analyzing Incorrect Data
197(1)
8.6 Different Users and Different Definitions
198(1)
8.7 Time Inconsistency of Data
199(1)
8.8 Data Stores and Data Transmission
200(2)
8.9 Retrieving Data from Production Systems
202(1)
8.10 Joining Historical and Operational Data
203(1)
8.11 Dealing with Organizational Changes
204(1)
8.12 Archiving Data
205(2)
Chapter 9 Data Virtualization and Service-Oriented Architecture
207(10)
9.1 Introduction
207(1)
9.2 Service-Oriented Architectures in a Nutshell
207(2)
9.3 Basic Services, Composite Services, Business Process Services, and Data Services
209(2)
9.4 Developing Data Services with a Data Virtualization Server
211(2)
9.5 Developing Composite Services with a Data Virtualization Server
213(2)
9.6 Services and the Internal Data Model
215(2)
Chapter 10 Data Virtualization and Master Data Management
217(14)
10.1 Introduction
217(1)
10.2 Data Is a Critical Asset for Every Organization
217(2)
10.3 The Need for a 360-Degree View of Business Objects
219(1)
10.4 What Is Master Data?
219(2)
10.5 What Is Master Data Management?
221(1)
10.6 A Master Data Management System
222(2)
10.7 Master Data Management for Integrating Data
224(1)
10.8 Integrating Master Data Management and Data Virtualization
224(7)
Chapter 11 Data Virtualization, Information Management, and Data Governance
231(12)
11.1 Introduction
231(1)
11.2 Impact of Data Virtualization on Information Modeling and Database Design
231(3)
11.3 Impact of Data Virtualization on Data Profiling
234(5)
11.4 Impact of Data Virtualization on Data Cleansing
239(1)
11.5 Impact of Data Virtualization on Data Governance
239(4)
Chapter 12 The Data Delivery Platform---A New Architecture for Business Intelligence Systems
243(10)
12.1 Introduction
243(1)
12.2 The Data Delivery Platform in a Nutshell
243(1)
12.3 The Definition of the Data Delivery Platform
244(1)
12.4 The Data Delivery Platform and Other Business Intelligence Architectures
245(2)
12.5 The Requirements of the Data Delivery Platform
247(2)
12.6 The Data Delivery Platform versus Data Virtualization
249(1)
12.7 Explanation of the Name
250(1)
12.8 A Personal Note
251(2)
Chapter 13 The Future of Data Visualization
253(14)
13.1 Introduction
253(1)
13.2 The Future of Data Virtualization According to Rick F. van der Lans
254(6)
13.2.1 New and Enhanced Query Optimization Techniques
254(1)
13.2.2 Exploiting New Hardware Technology
255(1)
13.2.3 Extending the Design Module
256(2)
13.2.4 Data Quality Features
258(1)
13.2.5 Support for the Push-Model for Data Access
258(1)
13.2.6 Blending of Data Virtualization, Extract Transform Load, Extract Load Transform, and Replication
259(1)
13.3 The Future of Data Virtualization According to David Besemer, CTO of Composite Software
260(2)
13.3.1 The Empowered Consumer Gains Ubiquitous Data Access
261(1)
13.3.2 IT's Back Office Becomes the Cloud
261(1)
13.3.3 Data Virtualization of the Future Is a Global Data Fabric
261(1)
13.3.4 Conclusion
262(1)
13.4 The Future of Data Virtualization According to Alberto Pan, CTO of Denodo Technologies
262(2)
13.5 The Future of Data Virtualization According to James Markarian, CTO of Informatica Corporation
264(3)
13.5.1 How to Maximize Return on Data with Data Virtualization
265(1)
13.5.2 Beyond Looking Under the Hood
266(1)
Bibliography 267(2)
Index 269
Rick F. van der Lans is an independent consultant, author, and lecturer specializing in business intelligence, data warehousing, and database technology. He is the managing director of R20/Consultancy which is based in The Netherlands. Rick has advised many large companies worldwide on defining their data warehouse architectures. He is the chairman of the annual European BI and Data Warehousing Conference organized in London, he writes for B-eye-Network.com, BI-platform.nl, and for Database Magazine. He is the author of several books on database technology, including Introduction to SQL (Addison-Wesley, 2006), currently in its fourth edition.