The Data Warehouse Toolkit

The Data Warehouse Toolkit

The Definitive Guide to Dimensional Modeling
3. Aufl.

von: Ralph Kimball, Margy Ross

46,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 01.07.2013
ISBN/EAN: 9781118530771
Sprache: englisch
Anzahl Seiten: 600

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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.
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
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.
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

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