Complete. The. Reference. Christopher Adamson. Mc Grauu. LlLIJBB. New York Chicago. San Francisco Lisbon London Madrid Mexico City

Transcription

1 The Complete Reference Christopher Adamson Mc Grauu LlLIJBB New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore Sydney Toronto

2 Contents Acknowledgments Introduction xvii xix Part I Fundamentals Chapter 1 Analytic Databases and Dimensional Design 3 Dimensional Design 3 Purpose 3 Measurement and Context 5 Facts and Dimensions 6 Grouping Dimensions and Facts 8 The Star Schema 10 Dimension Tables 10 Keys and History 11 Fact Tables 12 Using a Star Schema 12 Querying Facts 13 Browsing Dimensions 14 Guiding Principles 15 Summary 16 Further Reading 16 Chapter 2 Data Warehouse Architectures 17 Inmon's Corporate Information Factory 18 Kimball's Dimensional Data Warehouse 20 Stand-Alone Data Marts 22 Architecture and Dimensional Design 24 Contrasting the Approaches 24 The Common Element 26 Terms Used in This Book 27 Summary 28 Further Reading 28 Chapter 3 Stars and Cubes 29 Dimension Table Features 29 Surrogate Keys and Natural Keys 30 Rich Set of Dimensions 32 Grouping Dimensions into Dimension Tables 35 ix

3 X Star Schema: The Complete Reference Fact Table Features 38 Fact Tables and Processes 38 Capturing Facts 39 Grain 42 Sparsity 42 Degenerate Dimensions 43 Slowly Changing Dimensions 44 Type 1 Change 46 Type 2 Change 48 Choosing and Implementing Response Types 51 Cubes 53 Summary 56 Further Reading 57 Part li Multiple Stars Chapter 4 A Fact Table for Each Process 61 Fact Tables and Business Processes 61 Facts that Have Different Timing 62 A Single Fact Table Causes Difficulties 63 Modeling in Separate Fact Tables 66 Facts that Have Different Grain 67 A Single Fact Table Causes Difficulties 67 Modeling in Separate Fact Tables 70 Analyzing Facts from More than One Fact Table 71 The Peril of Joining Fact Tables 72 Drilling Across 73 Drill-Across Implementations 77 Summary 82 Further Reading 83 Chapter 5 Conformed Dimensions 85 The Synergy of Multiple Stars 85 Dimensions and Drilling Across 87 What Causes Failure? 88 Identical Tables Not Required 92 Conformed Dimensions 93 Types of Dimensional Conformance 93 Planning Conformance 100 Architecture and Conformance 102 Dimensional Data Warehouse 102 Corporate Information Factory 104 Stand-Alone Data Marts 106 Summary 108 Further Reading 109

4 Contents Xi Part III Dimension Design Chapter 6 More on Dimension Tables 113 Grouping Dimensions into Tables 114 Two Ways of Relating Dimension Attributes 114 When Struggling with Dimension Groupings 117 Breaking Up Large Dimensions 119 Splitting Dimension Tables Arbitrarily 120 Alternatives to Split Dimensions 122 Mini-Dimensions Alleviate ETL Bottlenecks and Excessive Growth 123 Dimension Roles and Aliasing 128 Avoiding the NULL 132 Problems Caused by NULL 132 Avoiding NULL Foreign Key Values 136 Uses for Special-Case Rows 138 Behavioral Dimensions 141 Converting Facts to Dimensions at Query Time 142 Designing and Using Behavioral Dimensions 142 Design Considerations for Behavioral Dimensions 144 Summary 144 Further Reading 145 Chapter 7 Hierarchies and Snowflakes 147 Drilling 148 The Concept of Drilling 148 The Reality of Drilling 149 Attribute Hierarchies and Drilling 149 The Attribute Hierarchy 149 Drilling Within an Attribute Hierarchy 150 Other Ways to Drill 151 Documenting Attribute Hierarchies 153 Snowflakes 157 Avoiding the Snowflake 158 Embracing the Snowflake 161 Outriggers 163 Repeating Groups 163 Eliminating Repeating Groups with Outriggers 165 Outriggers and Slow Change Processing 167 Summary 168 Further Reading 169 Chapter 8 More Slow Change Techniques 171 Time-Stamped Dimensions 172 Point-in-Time Status of a Dimension 172 The Time-Stamped Solution 175

5 Xii Star Schema: The Complete Reference Type 3 Changes 180 Study All Facts with Old or New Dimension Values 180 The Type 3 Solution 182 Hybrid Slow Changes 186 Conflicting Requirements 186 The Hybrid Response 187 Evaluating and Extending the Hybrid Approach 191 Summary 193 Further Reading 194 Chapter 9 Multi-Valued Dimensions and Bridges 195 Standard One-to-Many Relationships 196 Multi-Valued Dimensions 198 Simplifying the Relationship 198 Using a Bridge for Multi-Valued Dimensions 199 Multi-Valued Attributes 207 Simplifying the Multi-Valued Attribute 209 Using an Attribute Bridge 209 Summary 217 Further Reading 218 Chapter 10 Recursive Hierarchies and Bridges 219 Recursive Hierarchies 220 Rows Referring to Other Rows 220 The Reporting Challenge 222 Flattening a Recursive Hierarchy 223 A Flattened Hierarchy 224 Drawbacks of Flattening 225 When Flattening Works Best 226 The Hierarchy Bridge 227 Hierarchy Bridge Design 227 Using the Bridge 232 Double-Counting 235 Resolving the Many-to-Many Relationship 239 Potential Misuse 243 Changes and the Hierarchy Bridge 244 Type 1 Changes in the Dimension or Bridge 244 Type 2 Changes to the Dimension 245 Type 2 Changes to the Hierarchy 249 Variations on the Hierarchy Bridge 251 Embellishing the Bridge 251 Multiple Parents 253 Multiple Hierarchies 253 Summary 254 Further Reading 255

6 Contents xni Part IV Fact Table Design Chapter 11 Transactions, Snapshots, and Accumulating Snapshots 259 Transaction Fact Tables 260 Describing Events 260 Properties of Transaction Fact Tables 260 Snapshot Fact Tables 261 The Challenge: Studying Status 262 The Snapshot Model 265 Snapshot Considerations 269 Accumulating Snapshot Fact Tables 274 Challenge: Studying Elapsed Time Between Events 274 The Accumulating Snapshot 278 Accumulating Snapshot Considerations 282 Summary 287 Further Reading 288 Chapter 12 Factless Fact Tables 291 Events with No Facts 292 Nothing to Measure? 292 The Factless Fact Table 292 Using a Factless Fact Table 294 Adding a Fact 295 Conditions, Coverage, or Eligibility 297 Why Model Conditions? 298 Factless Fact Tables for Conditions 300 Comparing Activities and Conditions 301 Slowly Changing Dimensions and Conditions 304 Summary 305 Further Reading 305 Chapter 13 Type-Specific Stars 307 Type-Specific Attributes 308 Operational Systems 308 Analytic Systems 309 Core and Custom Stars 310 Core and Custom Dimension Tables 310 Core and Custom Fact Tables 314 Other Considerations 316 Using Generic Attributes 319 Generic Attributes 319 Using a Generic Design 321 Summary 322 Further Reading 322

7 XiV Star Schema: The Complete Reference PartV Performance Chapter 14 Derived Schemas 325 Restructuring Dimensional Data 326 Uses for Derived Schemas 326 Derived Schemas Already Covered 328 The Cost of Derived Schemas 329 The Merged Fact Table 330 Precomputed Drill-Across Results 331 Simplified Process Comparison 332 Improved Performance 332 Supporting Tools that Cannot Drill Across 333 Single-Process Analysis 333 Including a Nonshared Dimension 334 The Pivoted Fact Table 335 The Need to Pivot Data 335 The Pivoted Advantage 337 Drawbacks to Pivoting 337 The Sliced Fact Table 337 Creating Slices of a Star 338 Uses for Sliced Fact Tables 339 Slices First 339 Set Operation Fact Tables 340 Comparing Two Sets of Data 340 Several Possible Comparisons 341 Choosing to Precompute Set Operations 342 Summary 343 Further Reading 344 Chapter 15 Aggregates 345 Fundamentals of Aggregates 346 Summarizing Base Data 346 Using Aggregates 350 Loading Aggregates 353 Cubes as Aggregates 356 Making Aggregates Invisible 357 Aggregate Navigation 357 Aggregate Generation 360 Alternative Summary Designs 362 Transformative Summaries May Also Be Useful 362 Single Table Designs Should Be Avoided 363 Summary 365 Further Reading 366

8 Contents XV Part VI Tools and Documentation Chapter 16 Design and Business Intelligence 369 Business Intelligence and SQL Generation 370 SQL Generators 370 The Limitations of SQL Generators 373 Guidelines for the Semantic Layer 375 Features to Avoid 375 Features to Use 377 Working with SQL-Generating BI Tools 379 Multiple Stars 379 Semi-Additivity 385 Browse Queries 387 Bridge Tables 390 Working with Cube-Based BI 396 Cube-Centric Business Intelligence 396 Auto-Generation of Cubes 398 Summary 401 Further Reading 402 Chapter 17 Design and ETL 403 The ETL Process 404 A Complex Task 404 Tools Used by the ETL Process 404 Architecture and the ETL Process 404 Loading a Star 405 A Top-Level Dependency 405 Loading a Dimension Table 406 Loading the Fact Table 412 Optimizing the Load 417 Changed Data Identification 418 Simplifying Processing 419 Cleansing Data 420 What Should Be Cleaned Up 421 Cleaning Up Dimensional Data 422 Facts with Invalid Details 423 Housekeeping Columns 425 Housekeeping Columns in Dimension Tables 425 Housekeeping and Fact Tables 426 Summary 427 Further Reading 429 Chapter 18 How to Design and Document a Dimensional Model 431 Dimensional Design and the Data Warehouse Life Cycle 431 The Strategic Importance of Dimensional Design 432 When to Do Dimensional Design 434

9 XVi Star Schema: The Complete Reference Design Activities 434 Planning the Design Effort 435 Conducting Interviews 437 Designing the Dimensional Model 440 Prioritizing Plans 447 Documenting the Results 449 Documenting a Dimensional Model 449 Requirements Documentation 450 Top-Level Design Documentation 452 Detailed Design Documentation 458 Logical vs. Physical 461 Summary 462 Further Reading 463 Index 465