Data warehousing fundamentals for IT professionals:
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| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Hoboken [NJ]
Wiley
2010
|
| Edition: | 2. ed. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XXVIII, 571 S. Ill., graph Darst. |
| ISBN: | 9780470462072 |
Staff View
MARC
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| 100 | 1 | |a Ponniah, Paulraj |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Data warehousing fundamentals for IT professionals |c Paulraj Ponniah |
| 250 | |a 2. ed. | ||
| 264 | 1 | |a Hoboken [NJ] |b Wiley |c 2010 | |
| 300 | |a XXVIII, 571 S. |b Ill., graph Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 0 | 7 | |a Data-Warehouse-Konzept |0 (DE-588)4406462-7 |2 gnd |9 rswk-swf |
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Record in the Search Index
| _version_ | 1804143101334257664 |
|---|---|
| adam_text | CONTENTS
PREFACE
xxv
PART
1
OVERVIEW AND CONCEPTS
1
1
THE COMPELLING NEED FOR DATA WAREHOUSING
3
CHAPTER OBJECTIVES
/ 3
ESCALATING NEED FOR STRATEGIC INFORMATION
/ 4
The Information Crisis
/ 6
Technology Trends
/ 6
Opportunities and Risks
/ 8
FAILURES OF PAST DECISION-SUPPORT SYSTEMS
/ 9
History of Decision-Support Systems
/ 10
Inability to Provide Information
/ 10
OPERATIONAL VERSUS DECISION-SUPPORT SYSTEMS
/ 11
Making the Wheels of Business Turn
/ 12
Watching the Wheels of Business Turn
/ 12
Different Scope, Different Purposes
/ 12
DATA WAREHOUSING—THE ONLY VIABLE SOLUTION
/ 13
A New Type of System Environment
/13
Processing Requirements in the New Environment
/ 14
Strategic Information from the Data Warehouse
/ 14
VII
Vili
CONTENTS
DATA WAREHOUSE DEFINED
/ 15
A Simple Concept for Information Delivery
/15
An Environment, Not a Product
/15
A Blend of Many Technologies
/ 16
THE DATA WAREHOUSING MOVEMENT
/ 17
Data Warehousing Milestones
/17
Initial Challenges
/ 18
EVOLUTION OF BUSINESS INTELLIGENCE
/ 18
BI: Two Environments
/ 19
BI: Data Warehousing and Analytics
/ 19
CHAPTER SUMMARY
/ 20
REVIEW QUESTIONS
/ 20
EXERCISES
/21
2
DATA WAREHOUSE: THE BUILDING BLOCKS
23
CHAPTER OBJECTIVES
/ 23
DEFINING FEATURES
/ 24
Subject-Oriented Data
/ 24
Integrated Data
/ 25
Time-Variant Data
/ 26
Nonvolatile Data
/ 27
Data Granularity
/ 28
DATA WAREHOUSES AND DATA MARTS
/ 29
How Are They Different?
/ 29
Top-Down Versus Bottom-Up Approach
/ 29
A Practical Approach
/31
ARCHITECTURAL TYPES
/ 32
Centralized Data Warehouse
/ 32
Independent Data Marts
/ 32
Federated
/ 33
Hub-and-Spoke
/33
Data-Mart Bus
/ 34
OVERVIEW OF THE COMPONENTS
/ 34
Source Data Component
/ 34
Data Staging Component
/ 37
Data Storage Component
/ 39
Information Delivery Component
/ 40
Metadata Component
/41
Management and Control Component
/41
CONTENTS
ІХ
METADATA IN THE DATA WAREHOUSE
/ 41
Types of Metadata
/ 42
Special Significance
/ 42
CHAPTER SUMMARY
/ 42
REVIEW QUESTIONS
/ 43
EXERCISES
/ 43
3
TRENDS IN DATA WAREHOUSING
45
CHAPTER OBJECTIVES
/ 45
CONTINUED GROWTH IN DATA WAREHOUSING
/ 46
Data Warehousing has Become Mainstream
/ 46
Data Warehouse Expansion
/ 47
Vendor Solutions and Products
/ 48
SIGNIFICANT TRENDS
/ 50
Real-Time Data Warehousing
/ 50
Multiple Data Types
/ 50
Data Visualization
/ 52
Parallel Processing
/ 54
Data Warehouse Appliances
/ 56
Query Tools
/ 56
Browser Tools
/ 57
Data Fusion
/ 57
Data Integration
/ 58
Analytics
/ 59
Agent Technology
/ 59
Syndicated Data
/ 60
Data Warehousing and ERP
/ 60
Data Warehousing and KM
/61
Data Warehousing and CRM
/ 63
Agile Development
/ 63
Active Data Warehousing
/ 64
EMERGENCE OF STANDARDS
/ 64
Metadata
/ 65
OLAP / 65
WEB-ENABLED DATA WAREHOUSE
/ 66
The Warehouse to the Web
/ 67
The Web to the Warehouse
/ 67
The Web-Enabled Configuration
/ 69
CHAPTER SUMMARY
/ 69
X
CONTENTS
REVIEW
QUESTIONS
/ 69
EXERCISES
/ 70
PART
2
PLANNING
AND REQUIREMENTS
71
4
PLANNING AND PROJECT MANAGEMENT
73
CHAPTER
OBJECTIVES
/ 73
PLANNING
YOUR DATA WAREHOUSE
/ 74
Key
Issues
/ 74
Business
Requirements, Not
Technology
/ 76
Top Management Support
/ 77
Justifying Your Data Warehouse /
77
The Overall Plan
/ 78
THE DATA WAREHOUSE PROJECT
/ 79
How is it Different?
/ 79
Assessment of Readiness
/81
The Life-Cycle Approach
/ 81
THE DEVELOPMENT PHASES
/ 83
Adopting Agile Development
/ 84
THE PROJECT TEAM
/ 85
Organizing the Project Team
/ 85
Roles and Responsibilities
/ 86
Skills and Experience Levels
/ 87
User Participation
/ 88
PROJECT MANAGEMENT CONSIDERATIONS
/ 90
Guiding Principles
/91
Warning Signs
/ 92
Success Factors
/ 92
Anatomy of a Successful Project
/ 93
Adopt a Practical Approach
/ 94
CHAPTER SUMMARY
/ 96
REVIEW QUESTIONS
/ 96
EXERCISES
/ 97
5
DEFINING THE BUSINESS REQUIREMENTS
99
CHAPTER OBJECTIVES
/ 99
DIMENSIONAL ANALYSIS
/ 100
Usage of Information Unpredictable
/100
Dimensional Nature of Business Data
/ 101
Examples of Business Dimensions
/ 102
CONTENTS
ХІ
INFORMATION
PACKAGES—
A USEFUL
CONCEPT
/ 103
Requirements Not Fully Determinate
/104
Business Dimensions
/105
Dimension Hierarchies and Categories
/ 106
Key Business Metrics or Facts
/ 107
REQUIREMENTS GATHERING METHODS
/ 109
Types of Questions
/ 110
Arrangement of Questions
/
111
Interview Techniques
/
111
Adapting the
JAD
Methodology
/ 113
Using Questionnaires
/ 115
Review of Existing Documentation
/ 115
REQUIREMENTS DEFINITION: SCOPE AND CONTENT
/116
Data Sources
/ 117
Data Transformation
/ 117
Data Storage
/ 117
Information Delivery
/ 118
Information Package Diagrams
/ 118
Requirements Definition Document Outline
/ 118
CHAPTER SUMMARY
/119
REVIEW QUESTIONS
/119
EXERCISES
/ 120
REQUIREMENTS AS THE DRIVING FORCE FOR
DATA WAREHOUSING
121
CHAPTER OBJECTIVES
/121
DATA DESIGN
/ 122
Structure for Business Dimensions
/ 123
Structure for Key Measurements
/ 124
Levels of Detail
/ 125
THE ARCHITECTURAL PLAN
/ 125
Composition of the Components
/ 126
Special Considerations
/ 127
Tools and Products
/ 129
DATA STORAGE SPECIFICATIONS
/131
DBMS Selection
/ 132
Storage Sizing
/ 132
INFORMATION DELIVERY STRATEGY
/ 133
Queries and Reports
/ 134
Types of Analysis
/ 134
Information Distribution
/135
XU CONTENTS
Real Time Information Delivery
/135
Decision Support Applications
/135
Growth and Expansion
/ 136
CHAPTER SUMMARY
/ 136
REVIEW QUESTIONS
/ 136
EXERCISES
/ 137
PART
3
ARCHITECTURE AND INFRASTRUCTURE
139
7
ARCHITECTURAL COMPONENTS
141
CHAPTER OBJECTIVES
/ 141
UNDERSTANDING DATA WAREHOUSE ARCHITECTURE
/ 141
Architecture: Definitions
/ 142
Architecture in Three Major Areas
/ 142
DISTINGUISHING CHARACTERISTICS
/ 143
Different Objectives and Scope
/ 144
Data Content
/ 144
Complex Analysis and Quick Response
/ 145
Flexible and Dynamic
/ 145
Metadata-Driven
/ 146
ARCHITECTURAL FRAMEWORK
/ 146
Architecture Supporting Flow of Data
/ 146
The Management and Control Module
/147
TECHNICAL ARCHITECTURE
/ 148
Data Acquisition
/ 149
Datastorage
/ 152
Information Delivery
/154
ARCHITECTURAL TYPES
/ 156
Centralized Corporate Data Warehouse
/ 156
Independent Data Marts
/156
Federated
/ 159
Hub-and-Spoke
/ 159
Data-Mart Bus
/ 160
CHAPTER SUMMARY
/ 160
REVIEW QUESTIONS
/ 160
EXERCISES
/161
8
INFRASTRUCTURE AS THE FOUNDATION FOR
DATA WAREHOUSING
163
CHAPTER OBJECTIVES
/ 163
CONTENTS
ХІІІ
INFRASTRUCTURE
SUPPORTING ARCHITECTURE
/ 164
Operational Infrastructure
/ 165
Physical Infrastructure
/ 165
HARDWARE AND OPERATING SYSTEMS
/ 166
Mainframes
/ 167
Open System Servers
/168
NT Servers
/ 168
Platform Options
/ 168
Server Hardware
/ 177
DATABASE SOFTWARE
/181
Parallel Processing Options
/182
Selection of the DBMS
/ 184
COLLECTION OF TOOLS
/ 184
Architecture First, Then Tools
/ 186
Data Modeling
/ 186
Data Extraction
/ 187
Data Transformation
/ 187
Data Loading
/ 187
Data Quality
/ 187
Queries and Reports
/187
Dashboards
/ 187
Scorecards
/187
Online Analytical Processing
(OLAP)
/ 188
Alert Systems
/ 188
Middleware and Connectivity
/ 188
Data Warehouse Administration
/188
DATA WAREHOUSE APPLIANCES
/ 188
Evolution of DW Appliances
/ 189
Benefits of DW Appliances
/ 190
CHAPTER SUMMARY
/191
REVIEW QUESTIONS
/191
EXERCISES
/ 192
9
THE SIGNIFICANT ROLE OF METADATA
193
CHAPTER OBJECTIVES
/ 193
WHY METADATA IS IMPORTANT
/ 193
A Critical Need in the Data Warehouse
/195
Why Metadata Is Vital for End-Users
/ 198
Why Metadata Is Essential for IT
/ 199
Automation of Warehousing Tasks
/ 200
Establishing the Context of Information
/ 202
XIV
CONTENTS
METADATA TYPES BY FUNCTIONAL AREAS
/ 203
Data Acquisition
/ 204
Datastorage
/ 205
Information Delivery
/ 206
BUSINESS METADATA
/ 207
Content Overview
/ 207
Examples of Business Metadata
/ 208
Content Highlights
/ 209
Who Benefits?
/ 209
TECHNICAL METADATA
/ 209
Content Overview
/210
Examples of Technical Metadata
/ 210
Content Highlights
/211
Who Benefits?
/211
HOW TO PROVIDE METADATA
/212
Metadata Requirements
/ 212
Sources of Metadata
/ 214
Challenges for Metadata Management
/ 215
Metadata Repository
/ 215
Metadata Integration and Standards
/ 217
Implementation Options
/ 218
CHAPTER SUMMARY
/219
REVIEW QUESTIONS
/ 220
EXERCISES
/ 220
PART
4
DATA DESIGN AND DATA PREPARATION
223
10
PRINCIPLES OF DIMENSIONAL MODELING
225
CHAPTER OBJECTIVES
/ 225
FROM REQUIREMENTS TO DATA DESIGN
/ 225
Design Decisions
/ 226
Dimensional Modeling Basics
/ 226
Е
-R
Modeling Versus Dimensional Modeling
/ 230
Use of CASE Tools
/ 232
THE STAR SCHEMA
/ 232
Review of a Simple STAR Schema
/ 232
Inside a Dimension Table
/ 234
Inside the Fact Table
/ 236
The Factless Fact Table
/238
Data Granularity
/ 238
CONTENTS
XV
STAR SCHEMA KEYS / 239
Primary Keys
/ 239
Surrogate Keys / 240
Foreign Keys
/ 240
ADVANTAGES OF THE STAR SCHEMA
/ 241
Easy for Users to Understand
/241
Optimizes Navigation
/ 242
Most Suitable for Query Processing
/ 243
STARjoin and STARindex
/ 244
STAR SCHEMA: EXAMPLES
/ 244
Video Rental
/ 244
Supermarket
/ 244
Wireless Phone Service
/ 244
Auction Company
/ 244
CHAPTER SUMMARY
/ 246
REVIEW QUESTIONS
/ 247
EXERCISES
/ 247
11
DIMENSIONAL MODELING: ADVANCED TOPICS
249
CHAPTER OBJECTIVES
/ 249
UPDATES TO THE DIMENSION TABLES
/ 250
Slowly Changing Dimensions
/ 250
Type
1
Changes: Correction of Errors
/ 251
Type
2
Changes: Preservation of History
/ 252
Type
3
Changes: Tentative Soft Revisions
/ 253
MISCELLANEOUS DIMENSIONS
/ 255
Large Dimensions
/ 255
Rapidly Changing Dimensions
/ 256
Junk Dimensions
/258
THE SNOWFLAKE SCHEMA
/ 259
Options to Normalize
/ 259
Advantages and Disadvantages
/ 260
When to Snowflake
/ 262
AGGREGATE FACT TABLES
/ 262
Fact Table Sizes
/ 264
Need for Aggregates
/ 266
Aggregating Fact Tables
/ 266
Aggregation Options
/ 271
FAMILIES OF STARS
/ 272
Snapshot and Transaction Tables
/ 273
Core and Custom Tables
/ 274
XVI CONTENTS
Supporting Enterprise Value Chain or Value Circle
/ 274
Conforming Dimensions
/ 275
Standardizing Facts
/276
Summary of Family of STARS
/ 277
CHAPTER SUMMARY
/ 277
REVIEW QUESTIONS
/ 278
EXERCISES
/ 278
12
DATA EXTRACTION, TRANSFORMATION, AND LOADING
281
CHAPTER OBJECTIVES
/ 281
ETL OVERVIEW
/ 282
Most Important and Most Challenging
/ 282
Time Consuming and Arduous
/ 283
ETL REQUIREMENTS AND STEPS
/ 284
Key Factors
/ 285
DATA EXTRACTION
/ 286
Source Identification
/ 287
Data Extraction Techniques
/ 287
Evaluation of the Techniques
/ 294
DATA TRANSFORMATION
/ 295
Data Transformation: Basic Tasks
/ 296
Major Transformation Types
/ 297
Data Integration and Consolidation
/ 299
Transformation for Dimension Attributes
/ 301
How to Implement Transformation
/301
DATA LOADING
/ 302
Applying Data: Techniques and Processes
/ 303
Data Refresh Versus Update
/ 306
Procedure for Dimension Tables
/ 306
Fact Tables: History and Incremental Loads
/ 307
ETL SUMMARY
/ 308
ETL Tool Options
/ 308
Reemphasizing ETL Metadata
/ 309
ETL Summary and Approach
/ 310
OTHER INTEGRATION APPROACHES
/311
Enterprise Information Integration
(Eli)
/311
Enterprise Application Integration (EAI)
/ 312
CHAPTER SUMMARY
/313
REVIEW QUESTIONS
/313
EXERCISES
/314
CONTENTS XVII
13 DATA
QUALITY: A KEY TO SUCCESS
315
CHAPTER OBJECTIVES
/315
WHY IS DATA QUALITY CRITICAL?
/316
What Is Data Quality?
/316
Benefits of Improved Data Quality
/ 319
Types of Data Quality Problems
/ 320
DATA QUALITY CHALLENGES
/ 323
Sources of Data Pollution
/ 323
Validation of Names and Addresses
/ 325
Costs of Poor Data Quality
/325
DATA QUALITY TOOLS
/ 326
Categories of Data Cleansing Tools
/ 327
Error Discovery Features
/ 327
Data Correction Features
/ 327
The DBMS for Quality Control
/ 327
DATA QUALITY INITIATIVE
/ 328
Data Cleansing Decisions
/ 329
Who Should Be Responsible?
/ 330
The Purification Process
/ 333
Practical Tips on Data Quality
/334
MASTER DATA MANAGEMENT
(MDM)
/ 335
MDM
Categories
/ 335
MDM
Benefits
/ 335
MDM
and Data Warehousing
/ 336
CHAPTER SUMMARY
/ 336
REVIEW QUESTIONS
/ 336
EXERCISES
/ 337
PART
5
INFORMATION ACCESS AND DELIVERY
339
14
MATCHING INFORMATION TO THE CLASSES OF USERS
341
CHAPTER OBJECTIVES
/ 341
INFORMATION FROM THE DATA WAREHOUSE
/ 342
Data Warehouse Versus Operational Systems
/ 342
Information Potential
/ 344
User-Information Interface
/ 347
Industry Applications
/ 348
WHO WILL USE THE INFORMATION?
/ 349
Classes of Users
/ 349
XViii
CONTENTS
What They Need
/ 352
How to Provide Information
/ 354
INFORMATION DELIVERY
/ 356
Queries
/ 357
Reports
/ 358
Analysis
/ 359
Applications
/ 359
INFORMATION DELIVERY TOOLS
/ 360
The Desktop Environment
/ 360
Methodology for Tool Selection
/361
Tool Selection Criteria
/ 364
Information Delivery Framework
/ 365
INFORMATION DELIVERY: SPECIAL TOPICS
/ 366
Business Activity Monitoring (BAM)
/ 366
Dashboards and Scorecards
/ 367
CHAPTER SUMMARY
/ 371
REVIEW QUESTIONS
/ 371
EXERCISES
/ 372
15 OLAP IN
THE DATA WAREHOUSE
373
CHAPTER OBJECTIVES
/ 373
DEMAND FOR ONLINE ANALYTICAL PROCESSING
/ 374
Need for Multidimensional Analysis
/ 374
Fast Access and Powerful Calculations
/ 375
Limitations of Other Analysis Methods
/ 377
OLAP
is the Answer
/ 379
OLAP
Definitions and Rules
/ 379
OLAP
Characteristics
/ 382
MAJOR FEATURES AND FUNCTIONS
/ 382
General Features
/383
Dimensional Analysis
/ 383
What Are Hypercubes?
/ 386
Drill Down and Roll Up
/ 390
Slice and Dice or Rotation
/ 392
Uses and Benefits
/ 393
OLAP
MODELS
/ 393
Overview of Variations
/ 394
The MOLAP Model
/ 394
The ROLAP Model
/ 395
ROLAP Versus MOLAP
/ 397
CONTENTS
ХІХ
OLAP IMPLEMENTATION
CONSIDERATIONS
/ 398
Data Design
and Preparation
/ 399
Administration and Performance / 401
OLAP
Platforms
/ 402
OLAP Tools and Products / 402
Implementation Steps / 403
Examples of Typical Implementations
/ 404
CHAPTER SUMMARY
/ 404
REVIEW QUESTIONS
/ 405
EXERCISES
/ 405
16
DATAWAREHOUSINGANDTHEWEB
407
CHAPTER OBJECTIVES
/ 407
WEB-ENABLED DATA WAREHOUSE
/ 408
Why the Web?
/ 408
Convergence of Technologies
/ 410
Adapting the Data Warehouse for the Web
/411
The Web as a Data Source
/412
Clickstream Analysis
/ 413
WEB-BASED INFORMATION DELIVERY
/414
Expanded Usage
/414
New Information Strategies
/ 416
Browser Technology for the Data Warehouse
/ 418
Security Issues
/419
OLAP
AND THE WEB
/ 420
Enterprise
OLAP / 420
Web-OLAP
Approaches
/ 420
OLAP
Engine Design
/ 421
BUILDING A WEB-ENABLED DATA WAREHOUSE
/ 421
Nature of the Data Webhouse
/ 422
Implementation Considerations
/ 423
Putting the Pieces Together
/ 424
Web Processing Model
/ 426
CHAPTER SUMMARY
/ 426
REVIEW QUESTIONS
/ 426
EXERCISES
/ 427
17
DATA MINING BASICS
429
CHAPTER OBJECTIVES
/ 429
WHAT IS DATA MINING?
/ 430
XX
CONTENTS
Data
Mining Defined
/431
The Knowledge Discovery Process
/ 432
OLAP
Versus Data Mining
/ 435
Some Aspects of Data Mining
/ 436
Data Mining and the Data Warehouse
/438
MAJOR DATA MINING TECHNIQUES
/ 439
Cluster Detection
/ 440
Decision Trees
/ 443
Memory-Based Reasoning
/ 444
Link Analysis
/ 445
Neural Networks
/ 447
Genetic Algorithms
/ 448
Moving into Data Mining
/ 450
DATA MINING APPLICATIONS
/ 452
Benefits of Data Mining
/ 453
Applications in CRM (Customer Relationship Management)
/ 454
Applications in the Retail Industry
/ 455
Applications in the Telecommunications Industry
/ 456
Applications in Biotechnology
/ 457
Applications in Banking and Finance
/ 459
CHAPTER SUMMARY
/ 459
REVIEW QUESTIONS
/ 459
EXERCISES
/ 460
PART
6
IMPLEMENTATION AND MAINTENANCE
461
18
THE PHYSICAL DESIGN PROCESS
463
CHAPTER OBJECTIVES
/ 463
PHYSICAL DESIGN STEPS
/ 464
Develop Standards
/ 464
Create Aggregates Plan
/ 465
Determine the Data Partitioning Scheme
/ 465
Establish Clustering Options
/ 466
Prepare an Indexing Strategy
/ 466
Assign Storage Structures
/ 466
Complete Physical Model
/ 467
PHYSICAL DESIGN CONSIDERATIONS
/ 467
Physical Design Objectives
/ 467
From Logical Model to Physical Model
/ 469
CONTENTS
ХХІ
Physical
Model
Components
/ 469
Significance of Standards
/ 470
PHYSICAL STORAGE
/ 473
Storage Area Data Structures
/ 473
Optimizing Storage
/ 473
Using RAID Technology
/ 476
Estimating Storage Sizes
/ 477
INDEXING THE DATA WAREHOUSE
/ 477
Indexing Overview
/ 477
B-Tree Index
/ 479
Bitmapped Index
/481
Clustered Indexes
/ 482
Indexing the Fact Table
/ 482
Indexing the Dimension Tables
/ 483
PERFORMANCE ENHANCEMENT TECHNIQUES
/ 483
Data Partitioning
/ 483
Data Clustering
/ 484
Parallel Processing
/ 484
Summary Levels
/ 485
Referential Integrity Checks
/ 485
Initialization Parameters
/ 485
Data Arrays
/ 486
CHAPTER SUMMARY
/ 486
REVIEW QUESTIONS
/ 486
EXERCISES
/ 487
19
DATA WAREHOUSE DEPLOYMENT
489
CHAPTER OBJECTIVES
/ 489
DATA WAREHOUSE TESTING
/ 490
Front-End
/ 490
ETL Testing
/ 490
MAJOR DEPLOYMENT ACTIVITIES
/ 491
Complete User Acceptance
/491
Perform Initial Loads
/ 492
Get User Desktops Ready
/ 493
Complete Initial User Training
/ 494
Institute Initial User Support
/ 495
Deploy in Stages
/ 495
CONSIDERATIONS FOR A PILOT
/ 497
When is a Pilot Data Mart Useful?
/ 497
XX»
CONTENTS
Types of Pilot Projects
/ 498
Choosing the Pilot
/ 500
Expanding and Integrating the Pilot
/ 501
SECURITY
/ 502
Security Policy
/ 502
Managing User Privileges
/ 502
Password Considerations
/ 503
Security Tools
/ 504
BACKUP AND RECOVERY
/ 504
Why Back Up the Data Warehouse?
/ 505
Backup Strategy
/ 505
Setting up a Practical Schedule
/ 506
Recovery
/ 507
CHAPTER SUMMARY
/ 508
REVIEW QUESTIONS
/ 508
EXERCISES
/ 509
20
GROWTH AND MAINTENANCE
511
CHAPTER OBJECTIVES
/511
MONITORING THE DATA WAREHOUSE
/512
Collection of Statistics
/512
Using Statistics for Growth Planning
/514
Using Statistics for Fine-Tuning
/514
Publishing Trends for Users
/ 515
USER TRAINING AND SUPPORT
/515
User Training Content
/ 516
Preparing the Training Program
/ 516
Delivering the Training Program
/518
User Support
/519
MANAGING THE DATA WAREHOUSE
/ 520
Platform Upgrades
/ 521
Managing Data Growth
/521
Storage Management
/ 522
ETL Management
/ 522
Data Model Revisions
/ 523
Information Delivery Enhancements
/ 523
Ongoing Fine-Tuning
/524
CHAPTER SUMMARY
/ 524
REVIEW QUESTIONS
/ 525
EXERCISES
/ 525
CONTENTS
ХХІІІ
ANSWERS TO SELECTED EXERCISES
527
APPENDIX A: PROJECT LIFE CYCLE STEPS AND CHECKLISTS
531
APPENDIX B: CRITICAL FACTORS FOR SUCCESS
535
APPENDIX C: GUIDELINES FOR EVALUATING VENDOR SOLUTIONS
537
APPENDIX D: HIGHLIGHTS OF VENDORS AND PRODUCTS
539
APPENDIX E: REAL-WORLD EXAMPLES OF BEST PRACTICES
549
REFERENCES
555
GLOSSARY
557
INDEX
565
|
| any_adam_object | 1 |
| author | Ponniah, Paulraj |
| author_facet | Ponniah, Paulraj |
| author_role | aut |
| author_sort | Ponniah, Paulraj |
| author_variant | p p pp |
| building | Verbundindex |
| bvnumber | BV036526386 |
| classification_rvk | ST 530 |
| ctrlnum | (OCoLC)705629306 (DE-599)BVBBV036526386 |
| discipline | Informatik |
| edition | 2. ed. |
| format | Book |
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| id | DE-604.BV036526386 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T22:42:18Z |
| institution | BVB |
| isbn | 9780470462072 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-020448281 |
| oclc_num | 705629306 |
| open_access_boolean | |
| owner | DE-1050 DE-473 DE-BY-UBG |
| owner_facet | DE-1050 DE-473 DE-BY-UBG |
| physical | XXVIII, 571 S. Ill., graph Darst. |
| publishDate | 2010 |
| publishDateSearch | 2010 |
| publishDateSort | 2010 |
| publisher | Wiley |
| record_format | marc |
| spelling | Ponniah, Paulraj Verfasser aut Data warehousing fundamentals for IT professionals Paulraj Ponniah 2. ed. Hoboken [NJ] Wiley 2010 XXVIII, 571 S. Ill., graph Darst. txt rdacontent n rdamedia nc rdacarrier Data-Warehouse-Konzept (DE-588)4406462-7 gnd rswk-swf Data-Warehouse-Konzept (DE-588)4406462-7 s DE-604 Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020448281&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Ponniah, Paulraj Data warehousing fundamentals for IT professionals Data-Warehouse-Konzept (DE-588)4406462-7 gnd |
| subject_GND | (DE-588)4406462-7 |
| title | Data warehousing fundamentals for IT professionals |
| title_auth | Data warehousing fundamentals for IT professionals |
| title_exact_search | Data warehousing fundamentals for IT professionals |
| title_full | Data warehousing fundamentals for IT professionals Paulraj Ponniah |
| title_fullStr | Data warehousing fundamentals for IT professionals Paulraj Ponniah |
| title_full_unstemmed | Data warehousing fundamentals for IT professionals Paulraj Ponniah |
| title_short | Data warehousing fundamentals for IT professionals |
| title_sort | data warehousing fundamentals for it professionals |
| topic | Data-Warehouse-Konzept (DE-588)4406462-7 gnd |
| topic_facet | Data-Warehouse-Konzept |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020448281&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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