{"product_id":"the-data-warehouse-toolkit-isbn-9781118530801","title":"The Data Warehouse Toolkit","description":"\u003cp\u003e\u003cb\u003eUpdated new edition of Ralph Kimball's groundbreaking book on dimensional modeling for data warehousing and business intelligence!\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe first edition of Ralph Kimball's \u003ci\u003eThe Data Warehouse Toolkit\u003c\/i\u003e 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.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eAuthored by Ralph Kimball and Margy Ross, known worldwide as educators, consultants, and influential thought leaders in data warehousing and business intelligence\u003c\/li\u003e \u003cli\u003eBegins with fundamental design recommendations and progresses through increasingly complex scenarios\u003c\/li\u003e \u003cli\u003ePresents unique modeling techniques for business applications such as inventory management, procurement, invoicing, accounting, customer relationship management, big data analytics, and more\u003c\/li\u003e \u003cli\u003eDraws real-world case studies from a variety of industries, including retail sales, financial services, telecommunications, education, health care, insurance, e-commerce, and more\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eDesign dimensional databases that are easy to understand and provide fast query response with \u003ci\u003eThe Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition\u003c\/i\u003e.\u003c\/p\u003e \u003cp\u003eIntroduction xxvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Data Warehousing, Business Intelligence, and Dimensional Modeling Primer 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDifferent Worlds of Data Capture and Data Analysis 2\u003c\/p\u003e \u003cp\u003eGoals of Data Warehousing and Business Intelligence 3\u003c\/p\u003e \u003cp\u003eDimensional Modeling Introduction 7\u003c\/p\u003e \u003cp\u003eKimball’s DW\/BI Architecture 18\u003c\/p\u003e \u003cp\u003eAlternative DW\/BI Architectures 26\u003c\/p\u003e \u003cp\u003eDimensional Modeling Myths 30\u003c\/p\u003e \u003cp\u003eMore Reasons to Think Dimensionally 32\u003c\/p\u003e \u003cp\u003eAgile Considerations 34\u003c\/p\u003e \u003cp\u003eSummary 35\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 \u003c\/b\u003e\u003cb\u003eKimball Dimensional Modeling Techniques Overview 37\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFundamental Concepts 37\u003c\/p\u003e \u003cp\u003eBasic Fact Table Techniques 41\u003c\/p\u003e \u003cp\u003eBasic Dimension Table Techniques 46\u003c\/p\u003e \u003cp\u003eIntegration via Conformed Dimensions 50\u003c\/p\u003e \u003cp\u003eDealing with Slowly Changing Dimension Attributes 53\u003c\/p\u003e \u003cp\u003eDealing with Dimension Hierarchies 56\u003c\/p\u003e \u003cp\u003eAdvanced Fact Table Techniques 58\u003c\/p\u003e \u003cp\u003eAdvanced Dimension Techniques 62\u003c\/p\u003e \u003cp\u003eSpecial Purpose Schemas 67\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 \u003c\/b\u003e\u003cb\u003eRetail Sales 69\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFour-Step Dimensional Design Process 70\u003c\/p\u003e \u003cp\u003eRetail Case Study 72\u003c\/p\u003e \u003cp\u003eDimension Table Details 79\u003c\/p\u003e \u003cp\u003eRetail Schema in Action 94\u003c\/p\u003e \u003cp\u003eRetail Schema Extensibility 95\u003c\/p\u003e \u003cp\u003eFactless Fact Tables 97\u003c\/p\u003e \u003cp\u003eDimension and Fact Table Keys 98\u003c\/p\u003e \u003cp\u003eResisting Normalization Urges 104\u003c\/p\u003e \u003cp\u003eSummary 109\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 \u003c\/b\u003e\u003cb\u003eInventory 111\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eValue Chain Introduction 111\u003c\/p\u003e \u003cp\u003eInventory Models 112\u003c\/p\u003e \u003cp\u003eFact Table Types 119\u003c\/p\u003e \u003cp\u003eValue Chain Integration 122\u003c\/p\u003e \u003cp\u003eEnterprise Data Warehouse Bus Architecture 123\u003c\/p\u003e \u003cp\u003eConformed Dimensions 130\u003c\/p\u003e \u003cp\u003eConformed Facts 138\u003c\/p\u003e \u003cp\u003eSummary 139\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 \u003c\/b\u003e\u003cb\u003eProcurement 141\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eProcurement Case Study 141\u003c\/p\u003e \u003cp\u003eProcurement Transactions and Bus Matrix 142\u003c\/p\u003e \u003cp\u003eSlowly Changing Dimension Basics 147\u003c\/p\u003e \u003cp\u003eHybrid Slowly Changing Dimension Techniques 159\u003c\/p\u003e \u003cp\u003eSlowly Changing Dimension Recap 164\u003c\/p\u003e \u003cp\u003eSummary 165\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 \u003c\/b\u003e\u003cb\u003eOrder Management 167\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eOrder Management Bus Matrix 168\u003c\/p\u003e \u003cp\u003eOrder Transactions 168\u003c\/p\u003e \u003cp\u003eInvoice Transactions 187\u003c\/p\u003e \u003cp\u003eAccumulating Snapshot for Order Fulfillment Pipeline 194\u003c\/p\u003e \u003cp\u003eSummary 199\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 \u003c\/b\u003e\u003cb\u003eAccounting 201\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAccounting Case Study and Bus Matrix 202\u003c\/p\u003e \u003cp\u003eGeneral Ledger Data 203\u003c\/p\u003e \u003cp\u003eBudgeting Process 210\u003c\/p\u003e \u003cp\u003eDimension Attribute Hierarchies 214\u003c\/p\u003e \u003cp\u003eConsolidated Fact Tables 224\u003c\/p\u003e \u003cp\u003eRole of OLAP and Packaged Analytic Solutions 226\u003c\/p\u003e \u003cp\u003eSummary 227\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 \u003c\/b\u003e\u003cb\u003eCustomer Relationship Management 229\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCRM Overview 230\u003c\/p\u003e \u003cp\u003eCustomer Dimension Attributes 233\u003c\/p\u003e \u003cp\u003eBridge Tables for Multivalued Dimensions 245\u003c\/p\u003e \u003cp\u003eComplex Customer Behavior 249\u003c\/p\u003e \u003cp\u003eCustomer Data Integration Approaches 256\u003c\/p\u003e \u003cp\u003eLow Latency Reality Check 260\u003c\/p\u003e \u003cp\u003eSummary 261\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 \u003c\/b\u003e\u003cb\u003eHuman Resources Management 263\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEmployee Profile Tracking 263\u003c\/p\u003e \u003cp\u003eHeadcount Periodic Snapshot 267\u003c\/p\u003e \u003cp\u003eBus Matrix for HR Processes 268\u003c\/p\u003e \u003cp\u003ePackaged Analytic Solutions and Data Models 270\u003c\/p\u003e \u003cp\u003eRecursive Employee Hierarchies 271\u003c\/p\u003e \u003cp\u003eMultivalued Skill Keyword Attributes 274\u003c\/p\u003e \u003cp\u003eSurvey Questionnaire Data 277\u003c\/p\u003e \u003cp\u003eSummary 279\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 \u003c\/b\u003e\u003cb\u003eFinancial Services 281\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBanking Case Study and Bus Matrix 282\u003c\/p\u003e \u003cp\u003eDimension Triage to Avoid Too Few Dimensions 283\u003c\/p\u003e \u003cp\u003eSupertype and Subtype Schemas for Heterogeneous Products 293\u003c\/p\u003e \u003cp\u003eHot Swappable Dimensions 296\u003c\/p\u003e \u003cp\u003eSummary 296\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 \u003c\/b\u003e\u003cb\u003eTelecommunications 297\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTelecommunications Case Study and Bus Matrix 297\u003c\/p\u003e \u003cp\u003eGeneral Design Review Considerations 299\u003c\/p\u003e \u003cp\u003eDesign Review Guidelines 304\u003c\/p\u003e \u003cp\u003eDraft Design Exercise Discussion 306\u003c\/p\u003e \u003cp\u003eRemodeling Existing Data Structures 309\u003c\/p\u003e \u003cp\u003eGeographic Location Dimension 310\u003c\/p\u003e \u003cp\u003eSummary 310\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 \u003c\/b\u003e\u003cb\u003eTransportation 311\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAirline Case Study and Bus Matrix 311\u003c\/p\u003e \u003cp\u003eExtensions to Other Industries 317\u003c\/p\u003e \u003cp\u003eCombining Correlated Dimensions 318\u003c\/p\u003e \u003cp\u003eMore Date and Time Considerations 321\u003c\/p\u003e \u003cp\u003eLocalization Recap 324\u003c\/p\u003e \u003cp\u003eSummary 324\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 \u003c\/b\u003e\u003cb\u003eEducation 325\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUniversity Case Study and Bus Matrix 325\u003c\/p\u003e \u003cp\u003eAccumulating Snapshot Fact Tables 326\u003c\/p\u003e \u003cp\u003eFactless Fact Tables 329\u003c\/p\u003e \u003cp\u003eMore Educational Analytic Opportunities 336\u003c\/p\u003e \u003cp\u003eSummary 336\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 \u003c\/b\u003e\u003cb\u003eHealthcare 339\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHealthcare Case Study and Bus Matrix 339\u003c\/p\u003e \u003cp\u003eClaims Billing and Payments 342\u003c\/p\u003e \u003cp\u003eElectronic Medical Records 348\u003c\/p\u003e \u003cp\u003eFacility\/Equipment Inventory Utilization 351\u003c\/p\u003e \u003cp\u003eDealing with Retroactive Changes 351\u003c\/p\u003e \u003cp\u003eSummary 352\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 \u003c\/b\u003e\u003cb\u003eElectronic Commerce 353\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eClickstream Source Data 353\u003c\/p\u003e \u003cp\u003eClickstream Dimensional Models 357\u003c\/p\u003e \u003cp\u003eIntegrating Clickstream into Web Retailer’s Bus Matrix 368\u003c\/p\u003e \u003cp\u003eProfitability Across Channels Including Web 370\u003c\/p\u003e \u003cp\u003eSummary 373\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 \u003c\/b\u003e\u003cb\u003eInsurance 375\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eInsurance Case Study 376\u003c\/p\u003e \u003cp\u003ePolicy Transactions 379\u003c\/p\u003e \u003cp\u003ePremium Periodic Snapshot 385\u003c\/p\u003e \u003cp\u003eMore Insurance Case Study Background 388\u003c\/p\u003e \u003cp\u003eClaim Transactions 390\u003c\/p\u003e \u003cp\u003eClaim Accumulating Snapshot 392\u003c\/p\u003e \u003cp\u003ePolicy\/Claim Consolidated Periodic Snapshot 395\u003c\/p\u003e \u003cp\u003eFactless Accident Events 396\u003c\/p\u003e \u003cp\u003eCommon Dimensional Modeling Mistakes to Avoid 397\u003c\/p\u003e \u003cp\u003eSummary 401\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 \u003c\/b\u003e\u003cb\u003eKimball DW\/BI Lifecycle Overview 403\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eLifecycle Roadmap 404\u003c\/p\u003e \u003cp\u003eLifecycle Launch Activities 406\u003c\/p\u003e \u003cp\u003eLifecycle Technology Track 416\u003c\/p\u003e \u003cp\u003eLifecycle Data Track 420\u003c\/p\u003e \u003cp\u003eLifecycle BI Applications Track 422\u003c\/p\u003e \u003cp\u003eLifecycle Wrap-up Activities 424\u003c\/p\u003e \u003cp\u003eCommon Pitfalls to Avoid 426\u003c\/p\u003e \u003cp\u003eSummary 427\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 \u003c\/b\u003e\u003cb\u003eDimensional Modeling Process and Tasks 429\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eModeling Process Overview 429\u003c\/p\u003e \u003cp\u003eGet Organized 431\u003c\/p\u003e \u003cp\u003eDesign the Dimensional Model 434\u003c\/p\u003e \u003cp\u003eSummary 441\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 \u003c\/b\u003e\u003cb\u003eETL Subsystems and Techniques 443\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRound Up the Requirements 444\u003c\/p\u003e \u003cp\u003eThe 34 Subsystems of ETL 449\u003c\/p\u003e \u003cp\u003eExtracting: Getting Data into the Data Warehouse 450\u003c\/p\u003e \u003cp\u003eCleaning and Conforming Data 455\u003c\/p\u003e \u003cp\u003eDelivering: Prepare for Presentation 463\u003c\/p\u003e \u003cp\u003eManaging the ETL Environment 483\u003c\/p\u003e \u003cp\u003eSummary 496\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 \u003c\/b\u003e\u003cb\u003eETL System Design and Development Process and Tasks 497\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eETL Process Overview 497\u003c\/p\u003e \u003cp\u003eDevelop the ETL Plan 498\u003c\/p\u003e \u003cp\u003eDevelop One-Time Historic Load Processing 503\u003c\/p\u003e \u003cp\u003eDevelop Incremental ETL Processing 512\u003c\/p\u003e \u003cp\u003eReal-Time Implications 520\u003c\/p\u003e \u003cp\u003eSummary 526\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 \u003c\/b\u003e\u003cb\u003eBig Data Analytics 527\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBig Data Overview 527\u003c\/p\u003e \u003cp\u003eRecommended Best Practices for Big Data 531\u003c\/p\u003e \u003cp\u003eSummary 542\u003c\/p\u003e \u003cp\u003eIndex 543\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eRALPH KIMBALL\u003c\/b\u003e, PhD, has been a leading visionary in the data warehouse and business intelligence industry since 1982. \u003ci\u003eThe Data Warehouse Toolkit\u003c\/i\u003e book series have been bestsellers since 1996.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eMARGY ROSS\u003c\/b\u003e is President of the Kimball Group and the coauthor of five \u003ci\u003eToolkit\u003c\/i\u003e books with Ralph Kimball. She has focused exclusively on data warehousing and business intelligence for more than 30 years.\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eThe most authoritative and comprehensive guide to dimensional modeling, from its originators—fully updated\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRalph Kimball introduced the industry to the techniques of dimensional modeling in the first edition of \u003ci\u003eThe Data Warehouse Toolkit\u003c\/i\u003e (1996). Since then, dimensional modeling has become the most widely accepted approach for presenting information in data warehouse and business intelligence (DW\/BI) systems. \u003ci\u003eThe Data Warehouse Toolkit\u003c\/i\u003e is recognized as the definitive source for dimensional modeling techniques, patterns, and best practices.\u003c\/p\u003e \u003cp\u003eThis 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.\u003c\/p\u003e \u003cp\u003eAll 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.\u003c\/p\u003e \u003cp\u003eThe third edition of \u003ci\u003eThe Data Warehouse Toolkit\u003c\/i\u003e covers:\u003c\/p\u003e \u003cul\u003e \u003cli\u003ePractical design techniques—both basic and advanced—for dimension and fact tables\u003c\/li\u003e \u003cli\u003e14 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\u003c\/li\u003e \u003cli\u003eSample data warehouse bus matrices for 12 case studies\u003c\/li\u003e \u003cli\u003eDimensional modeling pitfalls and mistakes to avoid\u003c\/li\u003e \u003cli\u003eEnhanced slowly changing dimension techniques type 0 through 7\u003c\/li\u003e \u003cli\u003eBridge tables for ragged variable depth hierarchies and multivalued attributes\u003c\/li\u003e \u003cli\u003eBest practices for Big Data analytics\u003c\/li\u003e \u003cli\u003eGuidelines for collaborative, interactive design sessions with business stakeholders\u003c\/li\u003e \u003cli\u003eAn overview of the Kimball DW\/BI project lifecycle methodology\u003c\/li\u003e \u003cli\u003eComprehensive review of extract, transformation, and load (ETL) systems and design considerations\u003c\/li\u003e \u003cli\u003eThe 34 ETL subsystems and techniques to populate dimension and fact tables\u003c\/li\u003e \u003c\/ul\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47990204072165,"sku":"NP9781118530801","price":63.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118530801.jpg?v=1761786893","url":"https:\/\/k12savings.com\/es\/products\/the-data-warehouse-toolkit-isbn-9781118530801","provider":"K12savings","version":"1.0","type":"link"}