The Kimball Group Reader Relentlessly Practical Tools for Data Warehousing and Business Intelligence Remastered Collection 2nd Edition by Ralph Kimball, Margy Ross, Bob Becker, Joy Mundy, Warren Thornthwaite ISBN 1119216311 9781119216315

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ISBN 10: 1119216311 
ISBN 13: 9781119216315
Author: Ralph Kimball, Margy Ross, Bob Becker, Joy Mundy, Warren Thornthwaite

The Kimball Group Reader Relentlessly Practical Tools for Data Warehousing and Business Intelligence Remastered Collection 2nd Table of contents:

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

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Tags: Ralph Kimball, Margy Ross, Bob Becker, Joy Mundy, Warren Thornthwaite, Kimball Group, Data Warehousing