The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling

ISBN-13: 9781118530801 / Angielski / Miękka / 2013 / 608 str.

Ralph Kimball

Opis

Updated new edition of Ralph Kimball's groundbreaking book on dimensional modeling for data warehousing and business intelligence The first edition of Ralph Kimball's The Data Warehouse Toolkit introduced the industry to dimensional modeling, and now his books are considered the most authoritative guides in this space. This new third edition is a complete library of updated dimensional modeling techniques, the most comprehensive collection ever. It covers new and enhanced star schema dimensional modeling patterns, adds two new chapters on ETL techniques, includes new and expanded business matrices for 12 case studies, and more.

• Authored by Ralph Kimball and Margy Ross, known worldwide as educators, consultants, and influential thought leaders in data warehousing and business intelligence
• Begins with fundamental design recommendations and progresses through increasingly complex scenarios
• Presents unique modeling techniques for business applications such as inventory management, procurement, invoicing, accounting, customer relationship management, big data analytics, and more
• Draws real-world case studies from a variety of industries, including retail sales, financial services, telecommunications, education, health care, insurance, e-commerce, and more

Design dimensional databases that are easy to understand and provide fast query response with The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition.

Spis treści

Introduction xxvii

1 Data Warehousing, Business Intelligence, and Dimensional Modeling Primer 1                                     

Different Worlds of Data Capture and Data Analysis 2

Goals of Data Warehousing and Business Intelligence 3

Publishing Metaphor for DW/BI Managers 5

Dimensional Modeling Introduction 7

Star Schemas Versus OLAP Cubes 8

Fact Tables for Measurements 10

Dimension Tables for Descriptive Context 13

Facts and Dimensions Joined in a Star Schema 16

Kimball s DW/BI Architecture 18

Operational Source Systems 18

Extract, Transformation, and Load System 19

Presentation Area to Support Business Intelligence 21

Business Intelligence Applications 22

Restaurant Metaphor for the Kimball Architecture 23

Alternative DW/BI Architectures 26

Independent Data Mart Architecture 26

Hub–and–Spoke Corporate Information Factory Inmon Architecture 28

Hybrid Hub–and–Spoke and Kimball Architecture 29

Dimensional Modeling Myths 30

Myth 1: Dimensional Models are Only for Summary Data 30

Myth 2: Dimensional Models are Departmental, Not Enterprise 31

Myth 3: Dimensional Models are Not Scalable 31

Myth 4: Dimensional Models are Only for Predictable Usage 31

Myth 5: Dimensional Models Can t Be Integrated 32

More Reasons to Think Dimensionally 32

Agile Considerations 34

Summary

2 Kimball Dimensional Modeling Techniques Overview 37

Fundamental Concepts 37

Gather Business Requirements and Data Realities 37

Collaborative Dimensional Modeling Workshops 38

Four–Step Dimensional Design Process 38

Business Processes 39

Grain 39

Dimensions for Descriptive Context 40

Facts for Measurements 40

Star Schemas and OLAP Cubes 40

Graceful Extensions to Dimensional Models 41

Basic Fact Table Techniques 41

Fact Table Structure 41

Additive, Semi–Additive, Non–Additive Facts 42

Nulls in Fact Tables 42

Conformed Facts 42

Transaction Fact Tables 43

Periodic Snapshot Fact Tables 43

Accumulating Snapshot Fact Tables 44

Factless Fact Tables 44

Aggregate Fact Tables or OLAP Cubes 45

Consolidated Fact Tables 45

Basic Dimension Table Techniques 46

Dimension Table Structure 46

Dimension Surrogate Keys 46

Natural, Durable, and Supernatural Keys 46

Drilling Down 47

Degenerate Dimensions 47

Denormalized Flattened Dimensions 47

Multiple Hierarchies in Dimensions 48

Flags and Indicators as Textual Attributes 48

Null Attributes in Dimensions 48

Calendar Date Dimensions 48

Role–Playing Dimensions 49

Junk Dimensions 49

Snowflaked Dimensions 50

Outrigger Dimensions 50

Integration via Conformed Dimensions 50

Conformed Dimensions 51

Shrunken Dimensions 51

Drilling Across 51

Value Chain 52

Enterprise Data Warehouse Bus Architecture 52

Enterprise Data Warehouse Bus Matrix 52

Detailed Implementation Bus Matrix 53

Opportunity/Stakeholder Matrix 53

Dealing with Slowly Changing Dimension Attributes 53

Type 0: Retain Original 54

Type 1: Overwrite 54

Type 2: Add New Row 54

Type 3: Add New Attribute 55

Type 4: Add Mini–Dimension 55

Type 5: Add Mini–Dimension and Type 1 Outrigger 55

Type 6: Add Type 1 Attributes to Type 2 Dimension 56

Type 7: Dual Type 1 and Type 2 Dimensions 56

Dealing with Dimension Hierarchies 56

Fixed Depth Positional Hierarchies 56

Slightly Ragged/Variable Depth Hierarchies 57

Ragged/Variable Depth Hierarchies with Hierarchy Bridge Tables 57

Ragged/Variable Depth Hierarchies with Pathstring Attributes 57

Advanced Fact Table Techniques 58

Fact Table Surrogate Keys 58

Centipede Fact Tables 58

Numeric Values as Attributes or Facts 59

Lag/Duration Facts 59

Header/Line Fact Tables 59

Allocated Facts 60

Profit and Loss Fact Tables Using Allocations 60

Multiple Currency Facts 60

Multiple Units of Measure Facts 61

Year–to–Date Facts 61

Multipass SQL to Avoid Fact–to–Fact Table Joins 61

Timespan Tracking in Fact Tables 62

Late Arriving Facts 62

Advanced Dimension Techniques 62

Dimension–to–Dimension Table Joins 62

Multivalued Dimensions and Bridge Tables 63

Time Varying Multivalued Bridge Tables 63

Behavior Tag Time Series 63

Behavior Study Groups 64

Aggregated Facts as Dimension Attributes 64

Dynamic Value Bands 64

Text Comments Dimension 65

Multiple Time Zones 65

Measure Type Dimensions 65

Step Dimensions 65

Hot Swappable Dimensions 66

Abstract Generic Dimensions 66

Audit Dimensions 66

Late Arriving Dimensions 67

Special Purpose Schemas 67

Supertype and Subtype Schemas for Heterogeneous Products 67

Real–Time Fact Tables 68

Error Event Schemas 68

3 Retail Sales 69

Four–Step Dimensional Design Process 70

Step 1: Select the Business Process 70

Step 2: Declare the Grain 71

Step 3: Identify the Dimensions 72

Step 4: Identify the Facts 72

Retail Case Study 72

Step 1: Select the Business Process 74

Step 2: Declare the Grain 74

Step 3: Identify the Dimensions 76

Step 4: Identify the Facts 76

Dimension Table Details 79

Date Dimension 79

Product Dimension 83

Store Dimension 87

Promotion Dimension 89

Other Retail Sales Dimensions 92

Degenerate Dimensions for Transaction Numbers 93

Retail Schema in Action 94

Retail Schema Extensibility 95

Factless Fact Tables 97

Dimension and Fact Table Keys 98

Dimension Table Surrogate Keys 98

Dimension Natural and Durable Supernatural Keys 100

Degenerate Dimension Surrogate Keys 101

Date Dimension Smart Keys 101

Fact Table Surrogate Keys 102

Resisting Normalization Urges 104

Snowflake Schemas with Normalized Dimensions 104

Outriggers 106

Centipede Fact Tables with Too Many Dimensions 108

Summary 109

4 Inventory 111

Value Chain Introduction 111

Inventory Models 112

Inventory Periodic Snapshot 113

Inventory Transactions 116

Inventory Accumulating Snapshot 118

Fact Table Types 119

Transaction Fact Tables 120

Periodic Snapshot Fact Tables 120

Accumulating Snapshot Fact Tables 121

Complementary Fact Table Types 122

Value Chain Integration 122

Enterprise Data Warehouse Bus Architecture 123

Understanding the Bus Architecture 124

Enterprise Data Warehouse Bus Matrix 125

Conformed Dimensions 130

Drilling Across Fact Tables 130

Identical Conformed Dimensions 131

Shrunken Rollup Conformed Dimension with Attribute Subset 132

Shrunken Conformed Dimension with Row Subset 132

Shrunken Conformed Dimensions on the Bus Matrix 134

Limited Conformity 135

Importance of Data Governance and Stewardship 135

Conformed Dimensions and the Agile Movement 137

Conformed Facts 138

Summary 139

5 Procurement 141

Procurement Case Study 141

Procurement Transactions and Bus Matrix 142

Single Versus Multiple Transaction Fact Tables 143

Complementary Procurement Snapshot 147

Slowly Changing Dimension Basics 147

Type 0: Retain Original 148

Type 1: Overwrite 149

Type 2: Add New Row 150

Type 3: Add New Attribute 154

Type 4: Add Mini–Dimension 156

Hybrid Slowly Changing Dimension Techniques 159

Type 5: Mini–Dimension and Type 1 Outrigger 160

Type 6: Add Type 1 Attributes to Type 2 Dimension 160

Type 7: Dual Type 1 and Type 2 Dimensions 162

Slowly Changing Dimension Recap 164

Summary 165

6 Order Management 167

Order Management Bus Matrix 168

Order Transactions 168

Fact Normalization 169

Dimension Role Playing 170

Product Dimension Revisited 172

Customer Dimension 174

Deal Dimension 177

Degenerate Dimension for Order Number 178

Junk Dimensions 179

Header/Line Pattern to Avoid 181

Multiple Currencies 182

Transaction Facts at Different Granularity 184

Another Header/Line Pattern to Avoid 186

Invoice Transactions187

Service Level Performance as Facts, Dimensions, or Both 188

Profit and Loss Facts 189

Audit Dimension 192

Accumulating Snapshot for Order Fulfillment Pipeline 194

Lag Calculations 196

Multiple Units of Measure 197

Beyond the Rearview Mirror 198

Summary 199

7 Accounting 201

Accounting Case Study and Bus Matrix 202

General Ledger Data 203

General Ledger Periodic Snapshot 203

Chart of Accounts 203

Period Close 204

Year–to–Date Facts 206

Multiple Currencies Revisited 206

General Ledger Journal Transactions 206

Multiple Fiscal Accounting Calendars 208

Drilling Down Through a Multilevel Hierarchy 209

Financial Statements 209

Budgeting Process 210

Dimension Attribute Hierarchies 214

Fixed Depth Positional Hierarchies 214

Slightly Ragged Variable Depth Hierarchies 214

Ragged Variable Depth Hierarchies 215

Shared Ownership in a Ragged Hierarchy 219

Time Varying Ragged Hierarchies 220

Modifying Ragged Hierarchies 220

Alternative Ragged Hierarchy Modeling Approaches 221

Advantages of the Bridge Table Approach for Ragged Hierarchies 223

Consolidated Fact Tables 224

Role of OLAP and Packaged Analytic Solutions 226

Summary 227

8 Customer Relationship Management  229

CRM Overview 230

Operational and Analytic CRM 231

Customer Dimension Attributes 233

Name and Address Parsing 233

International Name and Address Considerations 236

Customer–Centric Dates 238

Aggregated Facts as Dimension Attributes 239

Segmentation Attributes and Scores 240

Counts with Type 2 Dimension Changes 243

Outrigger for Low Cardinality Attribute Set 243

Customer Hierarchy Considerations 244

Bridge Tables for Multivalued Dimensions 245

Bridge Table for Sparse Attributes 247

Bridge Table for Multiple Customer Contacts 248

Complex Customer Behavior 249

Behavior Study Groups for Cohorts 249

Step Dimension for Sequential Behavior 251

Timespan Fact Tables 252

Tagging Fact Tables with Satisfaction Indicators 254

Tagging Fact Tables with Abnormal Scenario Indicators 255

Customer Data Integration Approaches 256

Master Data Management Creating a Single Customer Dimension 256

Partial Conformity of Multiple Customer Dimensions 258

Avoiding Fact–to–Fact Table Joins 259

Low Latency Reality Check 260

Summary 261

9 Human Resources Management  263

Employee Profi le Tracking 263

Precise Effective and Expiration Timespans 265

Dimension Change Reason Tracking 266

Profi le Changes as Type 2 Attributes or Fact Events 267

Headcount Periodic Snapshot 267

Bus Matrix for HR Processes 268

Packaged Analytic Solutions and Data Models 270

Recursive Employee Hierarchies 271

Change Tracking on Embedded Manager Key 272

Drilling Up and Down Management Hierarchies 273

Multivalued Skill Keyword Attributes 274

Skill Keyword Bridge 275

Skill Keyword Text String 276

Survey Questionnaire Data 277

Text Comments 278

Summary 279

10 Financial Service 281

Banking Case Study and Bus Matrix 282

Dimension Triage to Avoid Too Few Dimensions 283

Household Dimension 286

Multivalued Dimensions and Weighting Factors 287

Mini–Dimensions Revisited 289

Adding a Mini–Dimension to a Bridge Table 290

Dynamic Value Banding of Facts 291

Supertype and Subtype Schemas for Heterogeneous Products 293

Supertype and Subtype Products with Common Facts 295

Hot Swappable Dimensions 296

Summary 296

11 Telecommunications  297

Telecommunications Case Study and Bus Matrix 297

General Design Review Considerations 299

Balance Business Requirements and Source Realities 300

Focus on Business Processes 300

Granularity 300

Single Granularity for Facts 301

Dimension Granularity and Hierarchies 301

Date Dimension 302

Degenerate Dimensions 303

Surrogate Keys 303

Dimension Decodes and Descriptions 303

Conformity Commitment 304

Design Review Guidelines 304

Draft Design Exercise Discussion 306

Remodeling Existing Data Structures 309

Geographic Location Dimension 310

Summary 310

12 Transportation  311

Airline Case Study and Bus Matrix 311

Multiple Fact Table Granularities 312

Linking Segments into Trips 315

Related Fact Tables 316

Extensions to Other Industries 317

Cargo Shipper 317

Travel Services 317

Combining Correlated Dimensions 318

Class of Service 319

Origin and Destination 320

More Date and Time Considerations 321

Country–Specific Calendars as Outriggers 321

Date and Time in Multiple Time Zones 323

Localization Recap 324

Summary 324

13 Education 325

University Case Study and Bus Matrix 325

Accumulating Snapshot Fact Tables 326

Applicant Pipeline 326

Research Grant Proposal Pipeline 329

Factless Fact Tables 329

Admissions Events 330

Course Registrations 330

Facility Utilization 334

Student Attendance 335

More Educational Analytic Opportunities 336

Summary 336

14 Healthcare 339

Healthcare Case Study and Bus Matrix 339

Claims Billing and Payments 342

Date Dimension Role Playing 345

Multivalued Diagnoses 345

Supertypes and Subtypes for Charges 347

Electronic Medical Records 348

Measure Type Dimension for Sparse Facts 349

Freeform Text Comments 350

Images 350

Facility/Equipment Inventory Utilization 351

Dealing with Retroactive Changes 351

Summary 352

15 Electronic Commerce 353

Clickstream Source Data 353

Clickstream Data Challenges 354

Clickstream Dimensional Models 357

Page Dimension 358

Event Dimension 359

Session Dimension 359

Referral Dimension 360

Clickstream Session Fact Table 361

Clickstream Page Event Fact Table 363

Step Dimension 366

Aggregate Clickstream Fact Tables 366

Google Analytics 367

Integrating Clickstream into Web Retailer s Bus Matrix 368

Profitability Across Channels Including Web 370

Summary 373

16 Insurance  375

Insurance Case Study 376

Insurance Value Chain 377

Draft Bus Matrix 378

Policy Transactions 379

Dimension Role Playing 380

Slowly Changing Dimensions 380

Mini–Dimensions for Large or Rapidly Changing Dimensions 381

Multivalued Dimension Attributes 382

Numeric Attributes as Facts or Dimensions 382

Degenerate Dimension 383

Low Cardinality Dimension Tables 383

Audit Dimension 383

Policy Transaction Fact Table 383

Heterogeneous Supertype and Subtype Products 384

Complementary Policy Accumulating Snapshot 384

Premium Periodic Snapshot 385

Conformed Dimensions 386

Conformed Facts 386

Pay–in–Advance Facts 386

Heterogeneous Supertypes and Subtypes Revisited 387

Multivalued Dimensions Revisited 388

More Insurance Case Study Background 388

Updated Insurance Bus Matrix 389

Detailed Implementation Bus Matrix 390

Claim Transactions 390

Transaction Versus Profile Junk Dimensions 392

Claim Accumulating Snapshot 392

Accumulating Snapshot for Complex Workflows 393

Timespan Accumulating Snapshot 394

Periodic Instead of Accumulating Snapshot 395

Policy/Claim Consolidated Periodic Snapshot 395

Factless Accident Events 396

Common Dimensional Modeling Mistakes to Avoid 397

Mistake 10: Place Text Attributes in a Fact Table 397

Mistake 9: Limit Verbose Descriptors to Save Space 398

Mistake 8: Split Hierarchies into Multiple Dimensions 398

Mistake 7: Ignore the Need to Track Dimension Changes 398

Mistake 6: Solve All Performance Problems with More Hardware 399

Mistake 5: Use Operational Keys to Join Dimensions and Facts 399

Mistake 4: Neglect to Declare and Comply with the Fact Grain 399

Mistake 3: Use a Report to Design the Dimensional Model 400

Mistake 2: Expect Users to Query Normalized Atomic Data 400

Mistake 1: Fail to Conform Facts and Dimensions 400

Summary 401

17 Kimball DW/BI Lifecycle Overview 403

Lifecycle Roadmap 404

Roadmap Mile Markers 405

Lifecycle Launch Activities 406

Program/Project Planning and Management 406

Business Requirements Definition 410

Lifecycle Technology Track 416

Technical Architecture Design 416

Product Selection and Installation 418

Lifecycle Data Track 420

Dimensional Modeling 420

Physical Design 420

ETL Design and Development 422

Lifecycle BI Applications Track 422

BI Application Specification 423

BI Application Development 423

Lifecycle Wrap–up Activities 424

Deployment 424

Maintenance and Growth 425

Common Pitfalls to Avoid 426

Summary 427

18 Dimensional Modeling Process and Tasks 429

Modeling Process Overview 429

Get Organized 431

Identify Participants, Especially Business Representatives 431

Review the Business Requirements 432

Leverage a Modeling Tool 432

Leverage a Data Profiling Tool 433

Leverage or Establish Naming Conventions 433

Coordinate Calendars and Facilities 433

Design the Dimensional Model 434

Reach Consensus on High–Level Bubble Chart 435

Develop the Detailed Dimensional Model 436

Review and Validate the Model 439

Finalize the Design Documentation 441

Summary 441

19 ETL Subsystems and Techniques 443

Round Up the Requirements 444

Business Needs 444

Compliance 445

Data Quality 445

Security 446

Data Integration 446

Data Latency 447

Archiving and Lineage 447

BI Delivery Interfaces 448

Available Skills 448

Legacy Licenses 449

The 34 Subsystems of ETL 449

Extracting: Getting Data into the Data Warehouse 450

Subsystem 1: Data Profiling 450

Subsystem 2: Change Data Capture System 451

Subsystem 3: Extract System 453

Cleaning and Conforming Data 455

Improving Data Quality Culture and Processes 455

Subsystem 4: Data Cleansing System 456

Subsystem 5: Error Event Schema 458

Subsystem 6: Audit Dimension Assembler 460

Subsystem 7: Deduplication System 460

Subsystem 8: Conforming System 461

Delivering: Prepare for Presentation 463

Subsystem 9: Slowly Changing Dimension Manager 464

Subsystem 10: Surrogate Key Generator 469

Subsystem 11: Hierarchy Manager 470

Subsystem 12: Special Dimensions Manager 470

Subsystem 13: Fact Table Builders 473

Subsystem 14: Surrogate Key Pipeline 475

Subsystem 15: Multivalued Dimension Bridge Table Builder 477

Subsystem 16: Late Arriving Data Handler 478

Subsystem 17: Dimension Manager System 479

Subsystem 18: Fact Provider System 480

Subsystem 19: Aggregate Builder 481

Subsystem 20: OLAP Cube Builder 481

Subsystem 21: Data Propagation Manager 482

Managing the ETL Environment 483

Subsystem 22: Job Scheduler 483

Subsystem 23: Backup System 485

Subsystem 24: Recovery and Restart System 486

Subsystem 25: Version Control System 488

Subsystem 26: Version Migration System 488

Subsystem 27: Workflow Monitor 489

Subsystem 28: Sorting System 490

Subsystem 29: Lineage and Dependency Analyzer 490

Subsystem 30: Problem Escalation System 491

Subsystem 31: Parallelizing/Pipelining System 492

Subsystem 32: Security System 492

Subsystem 33: Compliance Manager 493

Subsystem 34: Metadata Repository Manager 495

Summary 496

20 ETL System Design and Development Process and Tasks 497

ETL Process Overview 497

Develop the ETL Plan 498

Step 1: Draw the High–Level Plan 498

Step 2: Choose an ETL Tool 499

Step 3: Develop Default Strategies 500

Step 4: Drill Down by Target Table 500

Develop the ETL Specification Document 502

Develop One–Time Historic Load Processing 503

Step 5: Populate Dimension Tables with Historic Data 503

Step 6: Perform the Fact Table Historic Load 508

Develop Incremental ETL Processing 512

Step 7: Dimension Table Incremental Processing 512

Step 8: Fact Table Incremental Processing 515

Step 9: Aggregate Table and OLAP Loads 519

Step 10: ETL System Operation and Automation 519

Real–Time Implications 520

Real–Time Triage 521

Real–Time Architecture Trade–Offs 522

Real–Time Partitions in the Presentation Server 524

Summary 526

21 Big Data Analytics 527

Big Data Overview 527

Extended RDBMS Architecture 529

MapReduce/Hadoop Architecture 530

Comparison of Big Data Architectures 530

Recommended Best Practices for Big Data 531

Management Best Practices for Big Data 531

Architecture Best Practices for Big Data 533

Data Modeling Best Practices for Big Data 538

Data Governance Best Practices for Big Data 541

Summary 542

Index 543

O autorze

RALPH KIMBALL, PhD, has been a leading visionary in the data warehouse and business intelligence industry since 1982. The Data Warehouse Toolkit book series have been bestsellers since 1996.

MARGY ROSS is President of the Kimball Group and the coauthor of five Toolkit books with Ralph Kimball. She has focused exclusively on data warehousing and business intelligence for more than 30 years.

Blurb

The most authoritative and comprehensive guide to dimensional modeling, from its originators fully updated

Ralph Kimball introduced the industry to the techniques of dimensional modeling in the first edition of The Data Warehouse Toolkit (1996). Since then, dimensional modeling has become the most widely accepted approach for presenting information in data warehouse and business intelligence (DW/BI) systems. The Data Warehouse Toolkit is recognized as the definitive source for dimensional modeling techniques, patterns, and best practices.

This third edition of the classic reference delivers the most comprehensive library of dimensional modeling techniques ever assembled. Fully updated with fresh insights and best practices, this book provides clear guidelines for designing dimensional models and does so in a style that serves the needs of those new to data warehousing as well as experienced professionals.

All the techniques in the book are illustrated with real–world case studies based on the authors′ actual DW/BI design experiences. In addition, the Kimball Group′s "official" list of dimensional modeling techniques is summarized in a single chapter for easy reference, with pointers from each technique to the case studies where the concepts are brought to life.

The third edition of The Data Warehouse Toolkit covers:

• Practical design techniques both basic and advanced for dimension and fact tables
• 14 case studies, including retail sales, electronic commerce, customer relationship management, procurement, inventory, order management, accounting, human resources, financial services, healthcare, insurance, education, telecommunications, and transportation
• Sample data warehouse bus matrices for 12 case studies
• Dimensional modeling pitfalls and mistakes to avoid
• Enhanced slowly changing dimension techniques type 0 through 7
• Bridge tables for ragged variable depth hierarchies and multivalued attributes
• Best practices for Big Data analytics
• Guidelines for collaborative, interactive design sessions with business stakeholders
• An overview of the Kimball DW/BI project lifecycle methodology
• Comprehensive review of extract, transformation, and load (ETL) systems and design considerations
• The 34 ETL subsystems and techniques to populate dimension and fact tables