《Kimball集团读者》- 数据仓库与商业智能的实用工具

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"The Kimball Group Reader-Relentlessly Practical Tools" 是一本专注于数据仓库和商业智能领域的实践指南，由Ralph Kimball、Margy Ross以及Bob Becker、Joy Mundy和Warren Thornthwaite共同撰写。这本书涵盖了项目规划、需求定义、数据架构、维度建模、ETL（提取、转换、加载）过程、技术架构考虑等多个关键主题，旨在为读者提供实用的数据仓库和BI工具。在数据仓库方面，书中的"Project/Program Planning"章节讲述了如何有效规划和管理数据仓库项目，确保项目的顺利进行。"Requirements Definition"讨论了如何准确地定义业务需求，这是构建成功数据仓库的基础。"Data Architecture"部分则深入探讨了数据架构设计，包括如何设计支持高效查询和分析的体系结构。 "Dimensional Modeling Fundamentals"和"Dimensional Modeling Tasks and Responsibilities"是关于维度建模的中心内容。维度建模是数据仓库设计的核心技术，它简化了复杂数据，使得业务用户能够轻松理解和分析。书中介绍了星型模式、雪花模式等常见的维度模型，并详细阐述了构建和维护这些模型的职责与任务。 "Fact Table Core Concepts"和"Dimension Table Core Concepts"进一步深化了对事实表和维度表的理解，这两个组件构成了维度模型的基石。事实表存储测量的业务事件，而维度表提供了上下文，帮助解释这些测量值。 "More Dimension Patterns and Case Studies"章节提供了更多维度设计的示例和实际应用，帮助读者理解如何在不同场景下应用维度建模。 "Back Room ETL and Data Quality"章节聚焦于ETL过程，这是数据仓库建设中的关键环节，负责从源系统抽取数据，进行转换处理，然后加载到数据仓库中。同时，数据质量的保障也是这一阶段的重要工作，确保进入数据仓库的数据准确无误。 "Technical Architecture Considerations"则讨论了技术层面的考量，包括硬件选择、软件选型、性能优化等方面，以确保数据仓库系统的稳定性和可扩展性。《The Kimball Group Reader》是一本全面的指南，涵盖了数据仓库和BI领域的各个重要方面，无论是对于初学者还是经验丰富的专业人士，都能从中获得宝贵的知识和实践经验。

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Contents

xiv

7.2 Staffing the Dimensional Modeling Team..................216

7.3 Practical Steps for Designing a Dimensional Model . . . . . . . . . . 217

7.4 The Naming Game ...................................220

7.5 When Is the Dimensional Design Done? ..................221

Design Review Activities ....................................223

7.6 Fistful of Flaws ......................................223

7.7 Rating Your Dimensional Data Warehouse.................226

 8 FactTableCoreConcepts

233

Granularity...............................................233

8.1 Declaring the Grain...................................233

8.2 Keep to the Grain in Dimensional Modeling ...............236

8.3 Warning: Summary Data May Be Hazardous to Your Health ...238

8.4 No Detail Too Small ..................................239

Types of Fact Tables .......................................242

8.5 Fundamental Grains ..................................243

8.6 Modeling a Pipeline with an Accumulating Snapshot ........246

8.7 Combining Periodic and Accumulating Snapshots...........249

8.8 Modeling Time Spans .................................250

8.9 A Rolling Prediction of the Future, Now and in the Past ......252

8.10 Factless Fact Tables..................................255

8.11 Factless Fact Tables? Sound Like Jumbo Shrimp? ..........258

8.12 What Didn’t Happen .................................259

Parent-Child Fact Tables ....................................262

8.13 Managing Your Parents...............................263

8.14 Patterns to Avoid When Modeling Header/Line

Item Transactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266

Fact Table Keys and Degenerates..............................268

8.15 Fact Table Surrogate Keys.............................268

8.16 Reader Suggestions on Fact Table Surrogate Keys ..........269

8.17 Another Look at Degenerate Dimensions .................271

8.18 Creating a Reference Dimension for Infrequently

Accessed Degenerates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272

Miscellaneous Fact Table Design Patterns.......................273

8.19 Put Your Fact Tables on a Diet .........................273

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Contents

8.20 Keeping Text Out of the Fact Table .....................275

8.21 Dealing with Nulls in a Dimensional Model...............276

8.22 Modeling Data as Both a Fact and Dimension Attribute......277

8.23 When a Fact Table Can Be Used as a Dimension Table ......278

8.24 Sparse Facts and Facts with Short Lifetimes...............280

8.25 Pivoting the Fact Table with a Fact Dimension ............282

 9 DimensionTableCoreConcepts

285

Dimension Table Keys ......................................285

9.1 Surrogate Keys.......................................285

9.2 Keep Your Keys Simple................................289

Date and Time Dimension Considerations ......................290

9.3 It’s Time for Time ....................................291

9.4 Surrogate Keys for the Time Dimension...................293

9.5 Latest Thinking on Time Dimension Tables................295

9.6 Smart Date Keys to Partition Fact Tables ..................296

9.7 Handling All the Dates ................................298

Miscellaneous Dimension Patterns ............................299

9.8 Data Warehouse Role Models ...........................299

9.9 Mystery Dimensions ..................................303

9.10 De-Clutter with Junk Dimensions ......................306

9.11 Showing the Correlation Between Dimensions.............307

9.12 Causal (Not Casual) Dimensions .......................308

9.13 Resist Abstract Generic Dimensions .....................311

9.14 Hot-Swappable Dimensions ...........................312

9.15 Accurate Counting with a Dimensional Supplement ........314

Slowly Changing Dimensions ................................315

9.16 Perfectly Partitioning History with Type 2 SCD ............316

9.17 Many Alternate Realities ..............................316

9.18 Monster Dimensions .................................320

9.19 When a Slowly Changing Dimension Speeds Up ...........322

9.20 When Do Dimensions Become Dangerous? ...............325

9.21 Slowly Changing Dimensions Are Not Always as

Easy as 1, 2, and 3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326

9.22 Dimension Row Change Reason Attributes ...............330

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Contents

xvi

10 MoreDimensionPatternsandCaseStudies

333

Snowflakes, Outriggers, and Bridges ...........................333

10.1 Snowflakes, Outriggers, and Bridges.....................333

10.2 A Trio of Interesting Snowflakes........................336

10.3 Help for Dimensional Modeling ........................340

10.4 Managing Bridge Tables ..............................343

10.5 The Keyword Dimension .............................347

Dealing with Hierarchies ....................................351

10.6 Maintaining Dimension Hierarchies .....................351

10.7 Help for Hierarchies .................................355

10.8 Five Alternatives for Better Employee Dimensional Modeling ..359

10.9 Alternate Hierarchies ................................365

Customer Issues...........................................366

10.10 Dimension Embellishments ..........................367

10.11 Wrangling Behavior Tags ............................368

10.12 Three Ways to Capture Customer Satisfaction............371

Addresses and International Issues ............................374

10.13 Think Globally, Act Locally ..........................374

10.14 Warehousing without Borders ........................378

10.15 Spatially Enabling Your Data Warehouse ................383

10.16 Multinational Dimensional Data Warehouse Considerations..387

Industry Scenarios and Idiosyncrasies..........................389

10.17 An Insurance Data Warehouse Case Study...............389

10.18 Traveling through Databases..........................393

10.19 Human Resources Dimensional Models . . . . . . . . . . . . . . . . . 396

10.20 Not So Fast .......................................400

10.21 The Budgeting Chain ...............................403

10.22 Compliance-Enabled Data Warehouses .................407

10.23 Clicking with Your Customer.........................409

10.24 The Special Dimensions of the Clickstream . . . . . . . . . . . . . . 413

10.25 Fact Tables for Text Document Searching ...............417

10.26 Enabling Market Basket Analysis ......................420

11 BackRoomETLandDataQuality

425

Planning the ETL System....................................425

11.1 Surrounding the ETL Requirements .....................425

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Contents

xvii

11.2 The 34 Subsystems of ETL ............................430

11.3 Doing the Work at Extract Time........................434

11.4 Is Data Staging Relational? ............................437

11.5 Staging Areas and ETL Tools ..........................441

11.6 Should You Use an ETL Tool? .........................442

11.7 Document the ETL System ............................445

11.8 Measure Twice, Cut Once.............................445

11.9 Brace for Incoming ..................................449

11.10 Building a Change Data Capture System ................452

Data Quality Considerations .................................454

11.11 Dealing with Dirty Data .............................454

11.12 An Architecture for Data Quality ......................460

11.13 Indicators of Quality ................................468

11.14 Is Your Data Correct? ...............................471

11.15 Eight Recommendations for International Data Quality ....474

11.16 Using Regular Expressions for Data Cleaning ............477

Populating Fact and Dimension Tables .........................481

11.17 Pipelining Your Surrogates ...........................481

11.18 Replicating Dimensions Correctly .....................485

11.19 Identify Dimension Changes Using Cyclic

Redundancy Checksums. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 486

11.20 Maintaining Back Pointers to Operational Sources.........487

11.21 Creating Historical Dimension Rows ...................488

11.22 Backward in Time ..................................491

11.23 Early-Arriving Facts ................................494

11.24 Slowly Changing Entities ............................495

11.25 Creating, Using, and Maintaining Junk Dimensions .......497

11.26 Using the SQL MERGE for Slowly Changing Dimensions ...499

11.27 Being Offline as Little as Possible ......................502

Supporting Real Time ......................................503

11.28 Working in Web Time ..............................503

11.29 Real-Time Partitions ................................507

11.30 The Real-Time Triage ...............................510

12 TechnicalArchitectureConsiderations 

513

Overall Technical/System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . 513

12.1 Can the Data Warehouse Benefit from SOA? ..............513

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Contents

xviii

12.2 Picking the Right Approach to MDM . . . . . . . . . . . . . . . . . . . . 515

12.3 Building Custom Tools for the DW/BI System . . . . . . . . . . . . . 520

12.4 Welcoming the Packaged App .........................522

12.5 ERP Vendors: Bring Down Those Walls ..................525

12.6 Building a Foundation for Smart Applications .............528

12.7 RFID Tags and Smart Dust ............................533

Presentation Server Architecture ..............................535

12.8 The Aggregate Navigator..............................536

12.9 Aggregate Navigation with (Almost) No Metadata..........539

12.10 Relating to OLAP ..................................546

12.11 Dimensional Relational versus OLAP:

The Final Deployment Conundrum . . . . . . . . . . . . . . . . . . . . 549

12.12 Dimensional Modeling for Microsoft Analysis Services .....553

12.13 Architecting Your Data for Microsoft SQL Server 2005 .....554

12.14 Microsoft SQL Server Comes of Age for Data Warehousing..556

Front Room Architecture....................................560

12.15 The Second Revolution of User Interfaces ...............560

12.16 Designing the User Interface..........................562

Metadata.................................................566

12.17 Meta Meta Data Data................................567

12.18 Creating the Metadata Strategy ........................570

Infrastructure and Security Considerations......................572

12.19 Watching the Watchers..............................572

12.20 Catastrophic Failure ................................576

12.21 Digital Preservation.................................579

12.22 Creating the Advantages of a 64-Bit Server ..............582

12.23 Server Configuration Considerations ...................583

12.24 Adjust Your Thinking for SANs .......................585

13 FrontRoomBusinessIntelligenceApplications

589

Delivering Value with Business Intelligence .....................589

13.1 The Promise of Decision Support .......................589

13.2 Beyond Paving the Cow Paths..........................593

13.3 Big Shifts Happening in BI ............................596

13.4 Behavior: The Next Marquee Application.................598

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