Details

The Kimball Group Reader


The Kimball Group Reader

Relentlessly Practical Tools for Data Warehousing and Business Intelligence Remastered Collection
2. Aufl.

von: Ralph Kimball, Margy Ross, Bob Becker, Joy Mundy, Warren Thornthwaite

38,99 €

Verlag: Wiley
Format: EPUB
Veröffentl.: 01.02.2016
ISBN/EAN: 9781119216599
Sprache: englisch
Anzahl Seiten: 912

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Beschreibungen

<b>The final edition of the incomparable data warehousing and business intelligence reference, updated and expanded</b> <p><i>The Kimball Group Reader, Remastered Collection</i> is the essential reference for data warehouse and business intelligence design, packed with best practices, design tips, and valuable insight from industry pioneer Ralph Kimball and the Kimball Group. This Remastered Collection represents decades of expert advice and mentoring in data warehousing and business intelligence, and is the final work to be published by the Kimball Group. Organized for quick navigation and easy reference, this book contains nearly 20 years of experience on more than 300 topics, all fully up-to-date and expanded with 65 new articles. The discussion covers the complete data warehouse/business intelligence lifecycle, including project planning, requirements gathering, system architecture, dimensional modeling, ETL, and business intelligence analytics, with each group of articles prefaced by original commentaries explaining their role in the overall Kimball Group methodology. <p>Data warehousing/business intelligence industry's current multi-billion dollar value is due in no small part to the contributions of Ralph Kimball and the Kimball Group. Their publications are the standards on which the industry is built, and nearly all data warehouse hardware and software vendors have adopted their methods in one form or another. This book is a compendium of Kimball Group expertise, and an essential reference for anyone in the field. <ul> <li>Learn data warehousing and business intelligence from the field's pioneers</li> <li>Get up to date on best practices and essential design tips</li> <li>Gain valuable knowledge on every stage of the project lifecycle</li> <li>Dig into the Kimball Group methodology with hands-on guidance</li> </ul> <p>Ralph Kimball and the Kimball Group have continued to refine their methods and techniques based on thousands of hours of consulting and training. This Remastered Collection of <i>The Kimball Group Reader</i> represents their final body of knowledge, and is nothing less than a vital reference for anyone involved in the field.
<p>Introduction xxv</p> <p><b>1 </b><b>The <i>Reader </i>at a Glance 1</b></p> <p>Setting Up for Success 1</p> <p>1.1 Resist the Urge to Start Coding 1</p> <p>1.2 Set Your Boundaries 4</p> <p>Tackling DW/BI Design and Development 6</p> <p>1.3 Data Wrangling 6</p> <p>1.4 Myth Busters 9</p> <p>1.5 Dividing the World 10</p> <p>1.6 Essential Steps for the Integrated Enterprise Data Warehouse 13</p> <p>1.7 Drill Down to Ask Why 22</p> <p>1.8 Slowly Changing Dimensions 25</p> <p>1.9 Judge Your BI Tool through Your Dimensions 28</p> <p>1.10 Fact Tables 31</p> <p>1.11 Exploit Your Fact Tables 33</p> <p><b>2 </b><b>Before You Dive In 35</b></p> <p>Before Data Warehousing 35</p> <p>2.1 History Lesson on Ralph Kimball and Xerox PARC 36</p> <p>Historical Perspective 37</p> <p>2.2 The Database Market Splits 37</p> <p>2.3 Bringing Up Supermarts 40</p> <p>Dealing with Demanding Realities 47</p> <p>2.4 Brave New Requirements for Data Warehousing 47</p> <p>2.5 Coping with the Brave New Requirements 52</p> <p>2.6 Stirring Things Up 57</p> <p>2.7 Design Constraints and Unavoidable Realities 60</p> <p>2.8 Two Powerful Ideas 64</p> <p>2.9 Data Warehouse Dining Experience 67</p> <p>2.10 Easier Approaches for Harder Problems 70</p> <p>2.11 Expanding Boundaries of the Data Warehouse 72</p> <p><b>3 </b><b>Project/Program Planning 75</b></p> <p>Professional Responsibilities 75</p> <p>3.1 Professional Boundaries 75</p> <p>3.2 An Engineer’s View 78</p> <p>3.3 Beware the Objection Removers 82</p> <p>3.4 What Does the Central Team Do? 86</p> <p>3.5 Avoid Isolating DW and BI Teams 90</p> <p>3.6 Better Business Skills for BI and Data Warehouse Professionals 91</p> <p>3.7 Risky Project Resources are Risky Business 93</p> <p>3.8 Implementation Analysis Paralysis 95</p> <p>3.9 Contain DW/BI Scope Creep and Avoid Scope Theft 96</p> <p>3.10 Are IT Procedures Beneficial to DW/BI Projects? 98</p> <p>Justification and Sponsorship 100</p> <p>3.11 Habits of Effective Sponsors 100</p> <p>3.12 TCO Starts with the End User 103</p> <p>Kimball Methodology 108</p> <p>3.13 Kimball Lifecycle in a Nutshell 108</p> <p>3.14 Off the Bench111</p> <p>3.15 The Anti-Architect112</p> <p>3.16 Think Critically When Applying Best Practices 115</p> <p>3.17 Eight Guidelines for Low Risk Enterprise Data Warehousing 118</p> <p><b>4 </b><b>Requirements Definition 123</b></p> <p>Gathering Requirements 123</p> <p>4.1 Alan Alda’s Interviewing Tips for Uncovering Business Requirements 123</p> <p>4.2 More Business Requirements Gathering Dos and Don’ts 127</p> <p>4.3 Balancing Requirements and Realities 129</p> <p>4.4 Overcoming Obstacles When Gathering Business Requirements 130</p> <p>4.5 Surprising Value of Data Profiling 133</p> <p>Organizing around Business Processes 134</p> <p>4.6 Focus on Business Processes, Not Business Departments! 134</p> <p>4.7 Identifying Business Processes 135</p> <p>4.8 Business Process Decoder Ring 137</p> <p>4.9 Relationship between Strategic Business Initiatives and Business Processes 138</p> <p>Wrapping Up the Requirements 139</p> <p>4.10 The Bottom-Up Misnomer 140</p> <p>4.11 Think Dimensionally (Beyond Data Modeling) 144</p> <p>4.12 Using the Dimensional Model to Validate Business Requirements 145</p> <p><b>5 </b><b>Data Architecture 147</b></p> <p>Making the Case for Dimensional Modeling 147</p> <p>5.1 Is ER Modeling Hazardous to DSS? 147</p> <p>5.2 A Dimensional Modeling Manifesto 151</p> <p>5.3 There are No Guarantees 159</p> <p>Enterprise Data Warehouse Bus Architecture 163</p> <p>5.4 Divide and Conquer 163</p> <p>5.5 The Matrix 166</p> <p>5.6 The Matrix: Revisited 170</p> <p>5.7 Drill Down into a Detailed Bus Matrix 174</p> <p>Agile Project Considerations 176</p> <p>5.8 Relating to Agile Methodologies 176</p> <p>5.9 Is Agile Enterprise Data Warehousing an Oxymoron? 177</p> <p>5.10 Going Agile? Start with the Bus Matrix 179</p> <p>5.11 Conformed Dimensions as the Foundation for Agile Data Warehousing 180</p> <p>Integration Instead of Centralization 181</p> <p>5.12 Integration for Real People 181</p> <p>5.13 Build a Ready-to-Go Resource for Enterprise Dimensions 185</p> <p>5.14 Data Stewardship 101: The First Step to Quality and Consistency 186</p> <p>5.15 To Be or Not To Be Centralized 189</p> <p>Contrast with the Corporate Information Factory 192</p> <p>5.16 Differences of Opinion 193</p> <p>5.17 Much Ado about Nothing 198</p> <p>5.18 Don’t Support Business Intelligence with a Normalized EDW 199</p> <p>5.19 Complementing 3NF EDWs with Dimensional Presentation Areas 201</p> <p><b>6 </b><b>Dimensional Modeling Fundamentals 203</b></p> <p>Basics of Dimensional Modeling 203</p> <p>6.1 Fact Tables and Dimension Tables 203</p> <p>6.2 Drilling Down, Up, and Across 207</p> <p>6.3 The Soul of the Data Warehouse, Part One: Drilling Down 210</p> <p>6.4 The Soul of the Data Warehouse, Part Two: Drilling Across 213</p> <p>6.5 The Soul of the Data Warehouse, Part Three: Handling Time 216</p> <p>6.6 Graceful Modifications to Existing Fact and Dimension Tables 219</p> <p>Dos and Don’ts 220</p> <p>6.7 Kimball’s Ten Essential Rules of Dimensional Modeling 221</p> <p>6.8 What Not to Do 223</p> <p>Myths about Dimensional Modeling 226</p> <p>6.9 Dangerous Preconceptions 226</p> <p>6.10 Fables and Facts 228</p> <p><b>7 </b><b>Dimensional Modeling Tasks and Responsibilities 233</b></p> <p>Design Activities 233</p> <p>7.1 Letting the Users Sleep 233</p> <p>7.2 Practical Steps for Designing a Dimensional Model 240</p> <p>7.3 Staffing the Dimensional Modeling Team 243</p> <p>7.4 Involve Business Representatives in Dimensional Modeling 244</p> <p>7.5 Managing Large Dimensional Design Teams 246</p> <p>7.6 Use a Design Charter to Keep Dimensional Modeling Activities on Track 248</p> <p>7.7 The Naming Game 249</p> <p>7.8 What’s in a Name? 250</p> <p>7.9 When is the Dimensional Design Done? 253</p> <p>Design Review Activities 254</p> <p>7.10 Design Review Dos and Don’ts 255</p> <p>7.11 Fistful of Flaws 257</p> <p>7.12 Rating Your Dimensional Data Warehouse 260</p> <p><b>8 </b><b>Fact Table Core Concepts 267</b></p> <p>Granularity 267</p> <p>8.1 Declaring the Grain 267</p> <p>8.2 Keep to the Grain in Dimensional Modeling 270</p> <p>8.3 Warning: Summary Data May Be Hazardous to Your Health 272</p> <p>8.4 No Detail Too Small 273</p> <p>Types of Fact Tables 276</p> <p>8.5 Fundamental Grains 277</p> <p>8.6 Modeling a Pipeline with an Accumulating Snapshot 280</p> <p>8.7 Combining Periodic and Accumulating Snapshots 282</p> <p>8.8 Complementary Fact Table Types 284</p> <p>8.9 Modeling Time Spans 286</p> <p>8.10 A Rolling Prediction of the Future, Now and in the Past 289</p> <p>8.11 Timespan Accumulating Snapshot Fact Tables 293</p> <p>8.12 Is it a Dimension, a Fact, or Both? 294</p> <p>8.13 Factless Fact Tables 295</p> <p>8.14 Factless Fact Tables? Sounds Like Jumbo Shrimp? 298</p> <p>8.15 What Didn’t Happen 299</p> <p>8.16 Factless Fact Tables for Simplification 302</p> <p>Parent-Child Fact Tables 304</p> <p>8.17 Managing Your Parents 304</p> <p>8.18 Patterns to Avoid When Modeling Header/Line Item Transactions 307</p> <p>Fact Table Keys and Degenerate Dimensions 309</p> <p>8.19 Fact Table Surrogate Keys 309</p> <p>8.20 Reader Suggestions on Fact Table Surrogate Keys 310</p> <p>8.21 Another Look at Degenerate Dimensions 312</p> <p>8.22 Creating a Reference Dimension for Infrequently Accessed Degenerates 313</p> <p>Miscellaneous Fact Table Design Patterns 314</p> <p>8.23 Put Your Fact Tables on a Diet 314</p> <p>8.24 Keeping Text Out of the Fact Table 316</p> <p>8.25 Dealing with Nulls in a Dimensional Model 317</p> <p>8.26 Modeling Data as Both a Fact and Dimension Attribute 318</p> <p>8.27 When a Fact Table Can Be Used as a Dimension Table 319</p> <p>8.28 Sparse Facts and Facts with Short Lifetimes 321</p> <p>8.29 Pivoting the Fact Table with a Fact Dimension 323</p> <p>8.30 Accumulating Snapshots for Complex Workflows 324</p> <p><b>9 </b><b>Dimension Table Core Concepts 327</b></p> <p>Dimension Table Keys 327</p> <p>9.1 Surrogate Keys 327</p> <p>9.2 Keep Your Keys Simple 331</p> <p>9.3 Durable “Super-Natural” Keys 333</p> <p>Date and Time Dimension Considerations 334</p> <p>9.4 It’s Time for Time 335</p> <p>9.5 Surrogate Keys for the Time Dimension 337</p> <p>9.6 Latest Thinking on Time Dimension Tables 339</p> <p>9.7 Smart Date Keys to Partition Fact Tables 341</p> <p>9.8 Updating the Date Dimension 342</p> <p>9.9 Handling All the Dates 343</p> <p>Miscellaneous Dimension Patterns 345</p> <p>9.10 Selecting Default Values for Nulls 345</p> <p>9.11 Data Warehouse Role Models 347</p> <p>9.12 Mystery Dimensions 350</p> <p>9.13 De-Clutter with Junk Dimensions 353</p> <p>9.14 Showing the Correlation between Dimensions 354</p> <p>9.15 Causal (Not Casual) Dimensions 356</p> <p>9.16 Resist Abstract Generic Dimensions 359</p> <p>9.17 Hot-Swappable Dimensions 360</p> <p>9.18 Accurate Counting with a Dimensional Supplement 361</p> <p>Slowly Changing Dimensions 363</p> <p>9.19 Perfectly Partitioning History with Type 2 SCD 363</p> <p>9.20 Many Alternate Realities 364</p> <p>9.21 Monster Dimensions 367</p> <p>9.22 When a Slowly Changing Dimension Speeds Up 370</p> <p>9.23 When Do Dimensions Become Dangerous? 372</p> <p>9.24 Slowly Changing Dimensions are Not Always as Easy as 1, 2, and 3 373</p> <p>9.25 Slowly Changing Dimension Types 0, 4, 5, 6 and 7 378</p> <p>9.26 Dimension Row Change Reason Attributes 382</p> <p><b>10 </b><b>More Dimension Patterns and Considerations 385</b></p> <p>Snowflakes, Outriggers, and Bridges 385</p> <p>10.1 Snowflakes, Outriggers, and Bridges 385</p> <p>10.2 A Trio of Interesting Snowflakes 388</p> <p>10.3 Help for Dimensional Modeling 392</p> <p>10.4 Managing Bridge Tables 395</p> <p>10.5 The Keyword Dimension 399</p> <p>10.6 Potential Bridge (Table) Detours 403</p> <p>10.7 Alternatives for Multi-Valued Dimensions 405</p> <p>10.8 Adding a Mini-Dimension to a Bridge Table 407</p> <p>Dealing with Hierarchies 409</p> <p>10.9 Maintaining Dimension Hierarchies 409</p> <p>10.10 Help for Hierarchies 414</p> <p>10.11 Five Alternatives for Better Employee Dimensional Modeling 417</p> <p>10.12 Avoiding Alternate Organization Hierarchies 425</p> <p>10.13 Alternate Hierarchies 426</p> <p>Customer Issues 427</p> <p>10.14 Dimension Embellishments 427</p> <p>10.15 Wrangling Behavior Tags 429</p> <p>10.16 Three Ways to Capture Customer Satisfaction 431</p> <p>10.17 Extreme Status Tracking for Real-Time Customer Analysis 435</p> <p>Addresses and International Issues 439</p> <p>10.18 Think Globally, Act Locally 439</p> <p>10.19 Warehousing without Borders 443</p> <p>10.20 Spatially Enabling Your Data Warehouse 448</p> <p>10.21 Multinational Dimensional Data Warehouse Considerations 452</p> <p>Industry Scenarios and Idiosyncrasies 453</p> <p>10.22 Industry Standard Data Models Fall Short 453</p> <p>10.23 An Insurance Data Warehouse Case Study 455</p> <p>10.24 Traveling through Databases 460</p> <p>10.25 Human Resources Dimensional Models 463</p> <p>10.26 Managing Backlogs Dimensionally 467</p> <p>10.27 Not So Fast 468</p> <p>10.28 The Budgeting Chain 471</p> <p>10.29 Compliance-Enabled Data Warehouses 475</p> <p>10.30 Clicking with Your Customer 477</p> <p>10.31 The Special Dimensions of the Clickstream 482</p> <p>10.32 Fact Tables for Text Document Searching 485</p> <p>10.33 Enabling Market Basket Analysis 489</p> <p><b>11 </b><b>Back Room ETL and Data Quality 495</b></p> <p>Planning the ETL System 495</p> <p>11.1 Surrounding the ETL Requirements 495</p> <p>11.2 The 34 Subsystems of ETL 500</p> <p>11.3 Six Key Decisions for ETL Architectures 504</p> <p>11.4 Three ETL Compromises to Avoid 508</p> <p>11.5 Doing the Work at Extract Time 510</p> <p>11.6 Is Data Staging Relational? 513</p> <p>11.7 Staging Areas and ETL Tools 517</p> <p>11.8 Should You Use an ETL Tool? 518</p> <p>11.9 Call to Action for ETL Tool Providers 521</p> <p>11.10 Document the ETL System 522</p> <p>11.11 Measure Twice, Cut Once 523</p> <p>11.12 Brace for Incoming 527</p> <p>11.13 Building a Change Data Capture System 530</p> <p>11.14 Disruptive ETL Changes 531</p> <p>11.15 New Directions for ETL 533</p> <p>Data Quality Considerations 535</p> <p>11.16 Dealing With Data Quality: Don’t Just Sit There, Do Something! 535</p> <p>11.17 Data Warehouse Testing Recommendations 537</p> <p>11.18 Dealing with Dirty Data 539</p> <p>11.19 An Architecture for Data Quality 545</p> <p>11.20 Indicators of Quality: The Audit Dimension 553</p> <p>11.21 Adding an Audit Dimension to Track Lineage and Confi dence 556</p> <p>11.22 Add Uncertainty to Your Fact Table 559</p> <p>11.23 Have You Built Your Audit Dimension Yet? 560</p> <p>11.24 Is Your Data Correct? 562</p> <p>11.25 Eight Recommendations for International Data Quality 565</p> <p>11.26 Using Regular Expressions for Data Cleaning 568</p> <p>Populating Fact and Dimension Tables 572</p> <p>11.27 Pipelining Your Surrogates 572</p> <p>11.28 Unclogging the Fact Table Surrogate Key Pipeline 576</p> <p>11.29 Replicating Dimensions Correctly 579</p> <p>11.30 Identify Dimension Changes Using Cyclic Redundancy Checksums 580</p> <p>11.31 Maintaining Back Pointers to Operational Sources 581</p> <p>11.32 Creating Historical Dimension Rows 582</p> <p>11.33 Facing the Re-Keying Crisis 585</p> <p>11.34 Backward in Time 587</p> <p>11.35 Early-Arriving Facts 590</p> <p>11.36 Slowly Changing Entities 591</p> <p>11.37 Using the SQL MERGE Statement for Slowly Changing Dimensions 593</p> <p>11.38 Creating and Managing Shrunken Dimensions 595</p> <p>11.39 Creating and Managing Mini-Dimensions 597</p> <p>11.40 Creating, Using, and Maintaining Junk Dimensions 599</p> <p>11.41 Building Bridges 601</p> <p>11.42 Being Offl ine as Little as Possible 605</p> <p>Supporting Real Time 606</p> <p>11.43 Working in Web Time 606</p> <p>11.44 Real-Time Partitions 610</p> <p>11.45 The Real-Time Triage 613</p> <p><b>12 </b><b>Technical Architecture Considerations 617</b></p> <p>Overall Technical/System Architecture 617</p> <p>12.1 Can the Data Warehouse Benefi t from SOA? 617</p> <p>12.2 Picking the Right Approach to MDM 619</p> <p>12.3 Building Custom Tools for the DW/BI System 625</p> <p>12.4 Welcoming the Packaged App 626</p> <p>12.5 ERP Vendors: Bring Down Those Walls 629</p> <p>12.6 Building a Foundation for Smart Applications 632</p> <p>12.7 RFID Tags and Smart Dust 637</p> <p>12.8 Is Big Data Compatible with the Data Warehouse? 640</p> <p>12.9 The Evolving Role of the Enterprise Data Warehouse in the Era of Big Data Analytics 641</p> <p>12.10 Newly Emerging Best Practices for Big Data 659</p> <p>12.11 The Hyper-Granular Active Archive 670</p> <p>Presentation Server Architecture 672</p> <p>12.12 Columnar Databases: Game Changers for DW/BI Deployment 672</p> <p>12.13 There is no Database Magic 673</p> <p>12.14 Relating to OLAP 676</p> <p>12.15 Dimensional Relational versus OLAP: The Final Deployment Conundrum 679</p> <p>12.16 Microsoft SQL Server Comes of Age for Data Warehousing 682</p> <p>12.17 The Aggregate Navigator 686</p> <p>12.18 Aggregate Navigation with (Almost) No Metadata 690</p> <p>Front Room Architecture 697</p> <p>12.19 The Second Revolution of User Interfaces 697</p> <p>12.20 Designing the User Interface 700</p> <p>Metadata 704</p> <p>12.21 Meta Meta Data Data 704</p> <p>12.22 Creating the Metadata Strategy 708</p> <p>12.23 Leverage Process Metadata for Self-Monitoring DW Operations 709</p> <p>Infrastructure and Security Considerations 712</p> <p>12.24 Watching the Watchers 712</p> <p>12.25 Catastrophic Failure 716</p> <p>12.26 Digital Preservation 719</p> <p>12.27 Creating the Advantages of a 64-Bit Server 722</p> <p>12.28 Server Configuration Considerations 723</p> <p>12.29 Adjust Your Thinking for SANs 726</p> <p><b>13 </b><b>Front Room Business Intelligence Applications 729</b></p> <p>Delivering Value with Business Intelligence 729</p> <p>13.1 The Promise of Decision Support 730</p> <p>13.2 Beyond Paving the Cow Paths 733</p> <p>13.3 BI Components for Business Value 736</p> <p>13.4 Big Shifts Happening in BI 738</p> <p>13.5 Behavior: The Next Marquee Application 740</p> <p>Implementing the Business Intelligence Layer 743</p> <p>13.6 Three Critical Components for Successful Self-Service BI 743</p> <p>13.7 Leverage Data Visualization Tools, But Avoid Anarchy 745</p> <p>13.8 Think Like a Software Development Manager 747</p> <p>13.9 Standard Reports: Basics for Business Users 748</p> <p>13.10 Building and Delivering BI Reports 753</p> <p>13.11 The BI Portal 757</p> <p>13.12 Dashboards Done Right 759</p> <p>13.13 Don’t Be Overly Reliant on Your Data Access Tool’s Metadata 760</p> <p>13.14 Making Sense of the Semantic Layer 762</p> <p>Mining Data to Uncover Relationships 764</p> <p>13.15 Digging into Data Mining 764</p> <p>13.16 Preparing for Data Mining 766</p> <p>13.17 The Perfect Handoff 770</p> <p>13.18 Get Started with Data Mining Now 774</p> <p>13.19 Leverage Your Dimensional Model for Predictive Analytics 778</p> <p>13.20 Does Your Organization Need an Analytic Sandbox? 779</p> <p>Dealing with SQL 781</p> <p>13.21 Simple Drill Across in SQL 781</p> <p>13.22 An Excel Macro for Drilling Across 783</p> <p>13.23 The Problem with Comparisons 785</p> <p>13.24 SQL Roadblocks and Pitfalls 789</p> <p>13.25 Features for Query Tools 792</p> <p>13.26 Turbocharge Your Query Tools 794</p> <p>13.27 Smarter Data Warehouses 798</p> <p><b>14 </b><b>Maintenance and Growth Considerations 805</b></p> <p>Deploying Successfully 805</p> <p>14.1 Don’t Forget the Owner’s Manual 805</p> <p>14.2 Let’s Improve Our Operating Procedures 809</p> <p>14.3 Marketing the DW/BI System 811</p> <p>14.4 Coping with Growing Pains 812</p> <p>Sustaining for Ongoing Impact 816</p> <p>14.5 Data Warehouse Checkups 816</p> <p>14.6 Boosting Business Acceptance 822</p> <p>14.7 Educate Management to Sustain DW/BI Success 825</p> <p>14.8 Getting Your Data Warehouse Back on Track 828</p> <p>14.9 Upgrading Your BI Architecture 829</p> <p>14.10 Four Fixes for Legacy Data Warehouses 831</p> <p>14.11 A Data Warehousing Fitness Program for Lean Times 835</p> <p>14.12 Enjoy the Sunset 839</p> <p><b>15 </b><b>Final Thoughts 841</b></p> <p>Key Insights and Reminders 841</p> <p>15.1 Final Word of the Day: Collaboration 841</p> <p>15.2 Tried and True Concepts for DW/BI Success 843</p> <p>15.3 Key Tenets of the Kimball Method 845</p> <p>A Look to the Future 847</p> <p>15.4 The Future is Bright 847</p> <p>Article Index 853</p> <p>Index 861</p>
<p><b>Ralph Kimball, PhD,</b> founded the Kimball Group and is a leading visionary in the data warehousing industry. <p><b>Margy Ross,</b> President of the Kimball Group and DecisionWorks Consulting, has focused on DW/BI solutions since 1982.
<p><b>Updated guidelines for data warehousing and business intelligence</b> <p>Recognized and respected throughout the world as the most influential leaders in the data warehousing and business intelligence (DW/BI) industry, Ralph Kimball, Margy Ross and the Kimball Group set the industry standard with <i>The Kimball Group Reader</i>. Now in its second edition, this preeminent text has been updated with 65 new <i>Design Tips </i>and white papers to create a remarkable collection that spans more than two decades at the forefront of DW/BI innovation. <p>From project planning and requirements gathering to dimensional modeling, ETL, and BI applications, this indispensable collection covers everything you'll encounter in data warehousing and business intelligence. With incomparable articles that provide critical advice for successfully designing, deploying, and maintaining the DW/BI system, <i>The Kimball Group Reader</i> covers: <ul> <li> Dos and Don'ts for launching a DW/BI project and gathering requirements</li> <li> Essentials for the integrated enterprise data warehouse, including the bus architecture and matrix</li> <li> Granularity and three fundamental types of fact tables</li> <li> Slowly changing dimension techniques</li> <li> Snowflakes, outriggers, and bridge tables</li> <li> Dimensional modeling advanced patterns </li> <li> Backroom extract, transform, and load (ETL) subsystems and data quality</li> <li> BI application best practices</li> <li> Big data considerations</li> </ul> <p>This easily referenced and newly updated collection is invaluable if you're involved with data warehousing or business intelligence in any capacity. <p><b>Wiley Computer Publishing Timely. Practical. Reliable.</b>

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