Introduction xxv
1 The Reader at a Glance 1
Setting Up for Success 1
1.1 Resist the Urge to Start Coding 1
1.2 Set Your Boundaries 4
Tackling DW/BI Design and Development 6
1.3 Data Wrangling 6
1.4 Myth Busters 9
1.5 Dividing the World 10
1.6 Essential Steps for the Integrated Enterprise Data Warehouse 13
1.7 Drill Down to Ask Why 22
1.8 Slowly Changing Dimensions 25
1.9 Judge Your BI Tool through Your Dimensions 28
1.10 Fact Tables 31
1.11 Exploit Your Fact Tables 33
2 Before You Dive In 35
Before Data Warehousing 35
2.1 History Lesson on Ralph Kimball and Xerox PARC 36
Historical Perspective 37
2.2 The Database Market Splits 37
2.3 Bringing Up Supermarts 40
Dealing with Demanding Realities 47
2.4 Brave New Requirements for Data Warehousing 47
2.5 Coping with the Brave New Requirements 52
2.6 Stirring Things Up 57
2.7 Design Constraints and Unavoidable Realities 60
2.8 Two Powerful Ideas 64
2.9 Data Warehouse Dining Experience 67
2.10 Easier Approaches for Harder Problems 70
2.11 Expanding Boundaries of the Data Warehouse 72
3 Project/Program Planning 75
Professional Responsibilities 75
3.1 Professional Boundaries 75
3.2 An Engineer’s View 78
3.3 Beware the Objection Removers 82
3.4 What Does the Central Team Do? 86
3.5 Avoid Isolating DW and BI Teams 90
3.6 Better Business Skills for BI and Data Warehouse Professionals 91
3.7 Risky Project Resources are Risky Business 93
3.8 Implementation Analysis Paralysis 95
3.9 Contain DW/BI Scope Creep and Avoid Scope Theft 96
3.10 Are IT Procedures Beneficial to DW/BI Projects? 98
Justification and Sponsorship 100
3.11 Habits of Effective Sponsors 100
3.12 TCO Starts with the End User 103
Kimball Methodology 108
3.13 Kimball Lifecycle in a Nutshell 108
3.14 Off the Bench111
3.15 The Anti-Architect112
3.16 Think Critically When Applying Best Practices 115
3.17 Eight Guidelines for Low Risk Enterprise Data Warehousing 118
4 Requirements Definition 123
Gathering Requirements 123
4.1 Alan Alda’s Interviewing Tips for Uncovering Business Requirements 123
4.2 More Business Requirements Gathering Dos and Don’ts 127
4.3 Balancing Requirements and Realities 129
4.4 Overcoming Obstacles When Gathering Business Requirements 130
4.5 Surprising Value of Data Profiling 133
Organizing around Business Processes 134
4.6 Focus on Business Processes, Not Business Departments! 134
4.7 Identifying Business Processes 135
4.8 Business Process Decoder Ring 137
4.9 Relationship between Strategic Business Initiatives and Business Processes 138
Wrapping Up the Requirements 139
4.10 The Bottom-Up Misnomer 140
4.11 Think Dimensionally (Beyond Data Modeling) 144
4.12 Using the Dimensional Model to Validate Business Requirements 145
5 Data Architecture 147
Making the Case for Dimensional Modeling 147
5.1 Is ER Modeling Hazardous to DSS? 147
5.2 A Dimensional Modeling Manifesto 151
5.3 There are No Guarantees 159
Enterprise Data Warehouse Bus Architecture 163
5.4 Divide and Conquer 163
5.5 The Matrix 166
5.6 The Matrix: Revisited 170
5.7 Drill Down into a Detailed Bus Matrix 174
Agile Project Considerations 176
5.8 Relating to Agile Methodologies 176
5.9 Is Agile Enterprise Data Warehousing an Oxymoron? 177
5.10 Going Agile? Start with the Bus Matrix 179
5.11 Conformed Dimensions as the Foundation for Agile Data Warehousing 180
Integration Instead of Centralization 181
5.12 Integration for Real People 181
5.13 Build a Ready-to-Go Resource for Enterprise Dimensions 185
5.14 Data Stewardship 101: The First Step to Quality and Consistency 186
5.15 To Be or Not To Be Centralized 189
Contrast with the Corporate Information Factory 192
5.16 Differences of Opinion 193
5.17 Much Ado about Nothing 198
5.18 Don’t Support Business Intelligence with a Normalized EDW 199
5.19 Complementing 3NF EDWs with Dimensional Presentation Areas 201
6 Dimensional Modeling Fundamentals 203
Basics of Dimensional Modeling 203
6.1 Fact Tables and Dimension Tables 203
6.2 Drilling Down, Up, and Across 207
6.3 The Soul of the Data Warehouse, Part One: Drilling Down 210
6.4 The Soul of the Data Warehouse, Part Two: Drilling Across 213
6.5 The Soul of the Data Warehouse, Part Three: Handling Time 216
6.6 Graceful Modifications to Existing Fact and Dimension Tables 219
Dos and Don’ts 220
6.7 Kimball’s Ten Essential Rules of Dimensional Modeling 221
6.8 What Not to Do 223
Myths about Dimensional Modeling 226
6.9 Dangerous Preconceptions 226
6.10 Fables and Facts 228
7 Dimensional Modeling Tasks and Responsibilities 233
Design Activities 233
7.1 Letting the Users Sleep 233
7.2 Practical Steps for Designing a Dimensional Model 240
7.3 Staffing the Dimensional Modeling Team 243
7.4 Involve Business Representatives in Dimensional Modeling 244
7.5 Managing Large Dimensional Design Teams 246
7.6 Use a Design Charter to Keep Dimensional Modeling Activities on Track 248
7.7 The Naming Game 249
7.8 What’s in a Name? 250
7.9 When is the Dimensional Design Done? 253
Design Review Activities 254
7.10 Design Review Dos and Don’ts 255
7.11 Fistful of Flaws 257
7.12 Rating Your Dimensional Data Warehouse 260
8 Fact Table Core Concepts 267
Granularity 267
8.1 Declaring the Grain 267
8.2 Keep to the Grain in Dimensional Modeling 270
8.3 Warning: Summary Data May Be Hazardous to Your Health 272
8.4 No Detail Too Small 273
Types of Fact Tables 276
8.5 Fundamental Grains 277
8.6 Modeling a Pipeline with an Accumulating Snapshot 280
8.7 Combining Periodic and Accumulating Snapshots 282
8.8 Complementary Fact Table Types 284
8.9 Modeling Time Spans 286
8.10 A Rolling Prediction of the Future, Now and in the Past 289
8.11 Timespan Accumulating Snapshot Fact Tables 293
8.12 Is it a Dimension, a Fact, or Both? 294
8.13 Factless Fact Tables 295
8.14 Factless Fact Tables? Sounds Like Jumbo Shrimp? 298
8.15 What Didn’t Happen 299
8.16 Factless Fact Tables for Simplification 302
Parent-Child Fact Tables 304
8.17 Managing Your Parents 304
8.18 Patterns to Avoid When Modeling Header/Line Item Transactions 307
Fact Table Keys and Degenerate Dimensions 309
8.19 Fact Table Surrogate Keys 309
8.20 Reader Suggestions on Fact Table Surrogate Keys 310
8.21 Another Look at Degenerate Dimensions 312
8.22 Creating a Reference Dimension for Infrequently Accessed Degenerates 313
Miscellaneous Fact Table Design Patterns 314
8.23 Put Your Fact Tables on a Diet 314
8.24 Keeping Text Out of the Fact Table 316
8.25 Dealing with Nulls in a Dimensional Model 317
8.26 Modeling Data as Both a Fact and Dimension Attribute 318
8.27 When a Fact Table Can Be Used as a Dimension Table 319
8.28 Sparse Facts and Facts with Short Lifetimes 321
8.29 Pivoting the Fact Table with a Fact Dimension 323
8.30 Accumulating Snapshots for Complex Workflows 324
9 Dimension Table Core Concepts 327
Dimension Table Keys 327
9.1 Surrogate Keys 327
9.2 Keep Your Keys Simple 331
9.3 Durable “Super-Natural” Keys 333
Date and Time Dimension Considerations 334
9.4 It’s Time for Time 335
9.5 Surrogate Keys for the Time Dimension 337
9.6 Latest Thinking on Time Dimension Tables 339
9.7 Smart Date Keys to Partition Fact Tables 341
9.8 Updating the Date Dimension 342
9.9 Handling All the Dates 343
Miscellaneous Dimension Patterns 345
9.10 Selecting Default Values for Nulls 345
9.11 Data Warehouse Role Models 347
9.12 Mystery Dimensions 350
9.13 De-Clutter with Junk Dimensions 353
9.14 Showing the Correlation between Dimensions 354
9.15 Causal (Not Casual) Dimensions 356
9.16 Resist Abstract Generic Dimensions 359
9.17 Hot-Swappable Dimensions 360
9.18 Accurate Counting with a Dimensional Supplement 361
Slowly Changing Dimensions 363
9.19 Perfectly Partitioning History with Type 2 SCD 363
9.20 Many Alternate Realities 364
9.21 Monster Dimensions 367
9.22 When a Slowly Changing Dimension Speeds Up 370
9.23 When Do Dimensions Become Dangerous? 372
9.24 Slowly Changing Dimensions are Not Always as Easy as 1, 2, and 3 373
9.25 Slowly Changing Dimension Types 0, 4, 5, 6 and 7 378
9.26 Dimension Row Change Reason Attributes 382
10 More Dimension Patterns and Considerations 385
Snowflakes, Outriggers, and Bridges 385
10.1 Snowflakes, Outriggers, and Bridges 385
10.2 A Trio of Interesting Snowflakes 388
10.3 Help for Dimensional Modeling 392
10.4 Managing Bridge Tables 395
10.5 The Keyword Dimension 399
10.6 Potential Bridge (Table) Detours 403
10.7 Alternatives for Multi-Valued Dimensions 405
10.8 Adding a Mini-Dimension to a Bridge Table 407
Dealing with Hierarchies 409
10.9 Maintaining Dimension Hierarchies 409
10.10 Help for Hierarchies 414
10.11 Five Alternatives for Better Employee Dimensional Modeling 417
10.12 Avoiding Alternate Organization Hierarchies 425
10.13 Alternate Hierarchies 426
Customer Issues 427
10.14 Dimension Embellishments 427
10.15 Wrangling Behavior Tags 429
10.16 Three Ways to Capture Customer Satisfaction 431
10.17 Extreme Status Tracking for Real-Time Customer Analysis 435
Addresses and International Issues 439
10.18 Think Globally, Act Locally 439
10.19 Warehousing without Borders 443
10.20 Spatially Enabling Your Data Warehouse 448
10.21 Multinational Dimensional Data Warehouse Considerations 452
Industry Scenarios and Idiosyncrasies 453
10.22 Industry Standard Data Models Fall Short 453
10.23 An Insurance Data Warehouse Case Study 455
10.24 Traveling through Databases 460
10.25 Human Resources Dimensional Models 463
10.26 Managing Backlogs Dimensionally 467
10.27 Not So Fast 468
10.28 The Budgeting Chain 471
10.29 Compliance-Enabled Data Warehouses 475
10.30 Clicking with Your Customer 477
10.31 The Special Dimensions of the Clickstream 482
10.32 Fact Tables for Text Document Searching 485
10.33 Enabling Market Basket Analysis 489
11 Back Room ETL and Data Quality 495
Planning the ETL System 495
11.1 Surrounding the ETL Requirements 495
11.2 The 34 Subsystems of ETL 500
11.3 Six Key Decisions for ETL Architectures 504
11.4 Three ETL Compromises to Avoid 508
11.5 Doing the Work at Extract Time 510
11.6 Is Data Staging Relational? 513
11.7 Staging Areas and ETL Tools 517
11.8 Should You Use an ETL Tool? 518
11.9 Call to Action for ETL Tool Providers 521
11.10 Document the ETL System 522
11.11 Measure Twice, Cut Once 523
11.12 Brace for Incoming 527
11.13 Building a Change Data Capture System 530
11.14 Disruptive ETL Changes 531
11.15 New Directions for ETL 533
Data Quality Considerations 535
11.16 Dealing With Data Quality: Don’t Just Sit There, Do Something! 535
11.17 Data Warehouse Testing Recommendations 537
11.18 Dealing with Dirty Data 539
11.19 An Architecture for Data Quality 545
11.20 Indicators of Quality: The Audit Dimension 553
11.21 Adding an Audit Dimension to Track Lineage and Confi dence 556
11.22 Add Uncertainty to Your Fact Table 559
11.23 Have You Built Your Audit Dimension Yet? 560
11.24 Is Your Data Correct? 562
11.25 Eight Recommendations for International Data Quality 565
11.26 Using Regular Expressions for Data Cleaning 568
Populating Fact and Dimension Tables 572
11.27 Pipelining Your Surrogates 572
11.28 Unclogging the Fact Table Surrogate Key Pipeline 576
11.29 Replicating Dimensions Correctly 579
11.30 Identify Dimension Changes Using Cyclic Redundancy Checksums 580
11.31 Maintaining Back Pointers to Operational Sources 581
11.32 Creating Historical Dimension Rows 582
11.33 Facing the Re-Keying Crisis 585
11.34 Backward in Time 587
11.35 Early-Arriving Facts 590
11.36 Slowly Changing Entities 591
11.37 Using the SQL MERGE Statement for Slowly Changing Dimensions 593
11.38 Creating and Managing Shrunken Dimensions 595
11.39 Creating and Managing Mini-Dimensions 597
11.40 Creating, Using, and Maintaining Junk Dimensions 599
11.41 Building Bridges 601
11.42 Being Offl ine as Little as Possible 605
Supporting Real Time 606
11.43 Working in Web Time 606
11.44 Real-Time Partitions 610
11.45 The Real-Time Triage 613
12 Technical Architecture Considerations 617
Overall Technical/System Architecture 617
12.1 Can the Data Warehouse Benefi t from SOA? 617
12.2 Picking the Right Approach to MDM 619
12.3 Building Custom Tools for the DW/BI System 625
12.4 Welcoming the Packaged App 626
12.5 ERP Vendors: Bring Down Those Walls 629
12.6 Building a Foundation for Smart Applications 632
12.7 RFID Tags and Smart Dust 637
12.8 Is Big Data Compatible with the Data Warehouse? 640
12.9 The Evolving Role of the Enterprise Data Warehouse in the Era of Big Data Analytics 641
12.10 Newly Emerging Best Practices for Big Data 659
12.11 The Hyper-Granular Active Archive 670
Presentation Server Architecture 672
12.12 Columnar Databases: Game Changers for DW/BI Deployment 672
12.13 There is no Database Magic 673
12.14 Relating to OLAP 676
12.15 Dimensional Relational versus OLAP: The Final Deployment Conundrum 679
12.16 Microsoft SQL Server Comes of Age for Data Warehousing 682
12.17 The Aggregate Navigator 686
12.18 Aggregate Navigation with (Almost) No Metadata 690
Front Room Architecture 697
12.19 The Second Revolution of User Interfaces 697
12.20 Designing the User Interface 700
Metadata 704
12.21 Meta Meta Data Data 704
12.22 Creating the Metadata Strategy 708
12.23 Leverage Process Metadata for Self-Monitoring DW Operations 709
Infrastructure and Security Considerations 712
12.24 Watching the Watchers 712
12.25 Catastrophic Failure 716
12.26 Digital Preservation 719
12.27 Creating the Advantages of a 64-Bit Server 722
12.28 Server Configuration Considerations 723
12.29 Adjust Your Thinking for SANs 726
13 Front Room Business Intelligence Applications 729
Delivering Value with Business Intelligence 729
13.1 The Promise of Decision Support 730
13.2 Beyond Paving the Cow Paths 733
13.3 BI Components for Business Value 736
13.4 Big Shifts Happening in BI 738
13.5 Behavior: The Next Marquee Application 740
Implementing the Business Intelligence Layer 743
13.6 Three Critical Components for Successful Self-Service BI 743
13.7 Leverage Data Visualization Tools, But Avoid Anarchy 745
13.8 Think Like a Software Development Manager 747
13.9 Standard Reports: Basics for Business Users 748
13.10 Building and Delivering BI Reports 753
13.11 The BI Portal 757
13.12 Dashboards Done Right 759
13.13 Don’t Be Overly Reliant on Your Data Access Tool’s Metadata 760
13.14 Making Sense of the Semantic Layer 762
Mining Data to Uncover Relationships 764
13.15 Digging into Data Mining 764
13.16 Preparing for Data Mining 766
13.17 The Perfect Handoff 770
13.18 Get Started with Data Mining Now 774
13.19 Leverage Your Dimensional Model for Predictive Analytics 778
13.20 Does Your Organization Need an Analytic Sandbox? 779
Dealing with SQL 781
13.21 Simple Drill Across in SQL 781
13.22 An Excel Macro for Drilling Across 783
13.23 The Problem with Comparisons 785
13.24 SQL Roadblocks and Pitfalls 789
13.25 Features for Query Tools 792
13.26 Turbocharge Your Query Tools 794
13.27 Smarter Data Warehouses 798
14 Maintenance and Growth Considerations 805
Deploying Successfully 805
14.1 Don’t Forget the Owner’s Manual 805
14.2 Let’s Improve Our Operating Procedures 809
14.3 Marketing the DW/BI System 811
14.4 Coping with Growing Pains 812
Sustaining for Ongoing Impact 816
14.5 Data Warehouse Checkups 816
14.6 Boosting Business Acceptance 822
14.7 Educate Management to Sustain DW/BI Success 825
14.8 Getting Your Data Warehouse Back on Track 828
14.9 Upgrading Your BI Architecture 829
14.10 Four Fixes for Legacy Data Warehouses 831
14.11 A Data Warehousing Fitness Program for Lean Times 835
14.12 Enjoy the Sunset 839
15 Final Thoughts 841
Key Insights and Reminders 841
15.1 Final Word of the Day: Collaboration 841
15.2 Tried and True Concepts for DW/BI Success 843
15.3 Key Tenets of the Kimball Method 845
A Look to the Future 847
15.4 The Future is Bright 847
Article Index 853
Index 861
