{"product_id":"building-the-data-warehouse-isbn-9780764599446","title":"Building the Data Warehouse","description":"\u003cul\u003e \u003cli\u003eThe new edition of the classic bestseller that launched the data warehousing industry covers new approaches and technologies, many of which have been pioneered by Inmon himself\u003c\/li\u003e \u003cli\u003eIn addition to explaining the fundamentals of data warehouse systems, the book covers new topics such as methods for handling unstructured data in a data warehouse and storing data across multiple storage media\u003c\/li\u003e \u003cli\u003eDiscusses the pros and cons of relational versus multidimensional design and how to measure return on investment in planning data warehouse projects\u003c\/li\u003e \u003cli\u003eCovers advanced topics, including data monitoring and testing\u003c\/li\u003e \u003cli\u003eAlthough the book includes an extra 100 pages worth of valuable content, the price has actually been reduced from $65 to $55\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003ePreface xix\u003c\/p\u003e \u003cp\u003eAcknowledgments xxvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Evolution of Decision Support Systems 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Evolution 2\u003c\/p\u003e \u003cp\u003eThe Advent of DASD 4\u003c\/p\u003e \u003cp\u003ePC\/4GL Technology 4\u003c\/p\u003e \u003cp\u003eEnter the Extract Program 5\u003c\/p\u003e \u003cp\u003eThe Spider Web 6\u003c\/p\u003e \u003cp\u003eProblems with the Naturally Evolving Architecture 7\u003c\/p\u003e \u003cp\u003eLack of Data Credibility 7\u003c\/p\u003e \u003cp\u003eProblems with Productivity 9\u003c\/p\u003e \u003cp\u003eFrom Data to Information 12\u003c\/p\u003e \u003cp\u003eA Change in Approach 14\u003c\/p\u003e \u003cp\u003eThe Architected Environment 16\u003c\/p\u003e \u003cp\u003eData Integration in the Architected Environment 18\u003c\/p\u003e \u003cp\u003eWho Is the User? 20\u003c\/p\u003e \u003cp\u003eThe Development Life Cycle 20\u003c\/p\u003e \u003cp\u003ePatterns of Hardware Utilization 22\u003c\/p\u003e \u003cp\u003eSetting the Stage for Re-engineering 23\u003c\/p\u003e \u003cp\u003eMonitoring the Data Warehouse Environment 25\u003c\/p\u003e \u003cp\u003eSummary 28\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 The Data Warehouse Environment 29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Structure of the Data Warehouse 33\u003c\/p\u003e \u003cp\u003eSubject Orientation 34\u003c\/p\u003e \u003cp\u003eDay 1 to Day n Phenomenon 39\u003c\/p\u003e \u003cp\u003eGranularity 41\u003c\/p\u003e \u003cp\u003eThe Benefits of Granularity 42\u003c\/p\u003e \u003cp\u003eAn Example of Granularity 43\u003c\/p\u003e \u003cp\u003eDual Levels of Granularity 46\u003c\/p\u003e \u003cp\u003eExploration and Data Mining 50\u003c\/p\u003e \u003cp\u003eLiving Sample Database 50\u003c\/p\u003e \u003cp\u003ePartitioning as a Design Approach 53\u003c\/p\u003e \u003cp\u003ePartitioning of Data 53\u003c\/p\u003e \u003cp\u003eStructuring Data in the Data Warehouse 56\u003c\/p\u003e \u003cp\u003eAuditing and the Data Warehouse 61\u003c\/p\u003e \u003cp\u003eData Homogeneity and Heterogeneity 61\u003c\/p\u003e \u003cp\u003ePurging Warehouse Data 64\u003c\/p\u003e \u003cp\u003eReporting and the Architected Environment 64\u003c\/p\u003e \u003cp\u003eThe Operational Window of Opportunity 65\u003c\/p\u003e \u003cp\u003eIncorrect Data in the Data Warehouse 67\u003c\/p\u003e \u003cp\u003eSummary 69\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 The Data Warehouse and Design 71\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBeginning with Operational Data 71\u003c\/p\u003e \u003cp\u003eProcess and Data Models and the Architected Environment 78\u003c\/p\u003e \u003cp\u003eThe Data Warehouse and Data Models 79\u003c\/p\u003e \u003cp\u003eThe Data Warehouse Data Model 81\u003c\/p\u003e \u003cp\u003eThe Midlevel Data Model 84\u003c\/p\u003e \u003cp\u003eThe Physical Data Model 88\u003c\/p\u003e \u003cp\u003eThe Data Model and Iterative Development 91\u003c\/p\u003e \u003cp\u003eNormalization and Denormalization 94\u003c\/p\u003e \u003cp\u003eSnapshots in the Data Warehouse 100\u003c\/p\u003e \u003cp\u003eMetadata 102\u003c\/p\u003e \u003cp\u003eManaging Reference Tables in a Data Warehouse 103\u003c\/p\u003e \u003cp\u003eCyclicity of Data — The Wrinkle of Time 105\u003c\/p\u003e \u003cp\u003eComplexity of Transformation and Integration 108\u003c\/p\u003e \u003cp\u003eTriggering the Data Warehouse Record 112\u003c\/p\u003e \u003cp\u003eEvents 112\u003c\/p\u003e \u003cp\u003eComponents of the Snapshot 113\u003c\/p\u003e \u003cp\u003eSome Examples 113\u003c\/p\u003e \u003cp\u003eProfile Records 114\u003c\/p\u003e \u003cp\u003eManaging Volume 115\u003c\/p\u003e \u003cp\u003eCreating Multiple Profile Records 117\u003c\/p\u003e \u003cp\u003eGoing from the Data Warehouse to the Operational Environment 117\u003c\/p\u003e \u003cp\u003eDirect Operational Access of Data Warehouse Data 118\u003c\/p\u003e \u003cp\u003eIndirect Access of Data Warehouse Data 119\u003c\/p\u003e \u003cp\u003eAn Airline Commission Calculation System 119\u003c\/p\u003e \u003cp\u003eA Retail Personalization System 121\u003c\/p\u003e \u003cp\u003eCredit Scoring 123\u003c\/p\u003e \u003cp\u003eIndirect Use of Data Warehouse Data 125\u003c\/p\u003e \u003cp\u003eStar Joins 126\u003c\/p\u003e \u003cp\u003eSupporting the ODS 133\u003c\/p\u003e \u003cp\u003eRequirements and the Zachman Framework 134\u003c\/p\u003e \u003cp\u003eSummary 136\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Granularity in the Data Warehouse 139\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRaw Estimates 140\u003c\/p\u003e \u003cp\u003eInput to the Planning Process 141\u003c\/p\u003e \u003cp\u003eData in Overflow 142\u003c\/p\u003e \u003cp\u003eOverflow Storage 144\u003c\/p\u003e \u003cp\u003eWhat the Levels of Granularity Will Be 147\u003c\/p\u003e \u003cp\u003eSome Feedback Loop Techniques 148\u003c\/p\u003e \u003cp\u003eLevels of Granularity — Banking Environment 150\u003c\/p\u003e \u003cp\u003eFeeding the Data Marts 157\u003c\/p\u003e \u003cp\u003eSummary 157\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 The Data Warehouse and Technology 159\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eManaging Large Amounts of Data 159\u003c\/p\u003e \u003cp\u003eManaging Multiple Media 161\u003c\/p\u003e \u003cp\u003eIndexing and Monitoring Data 162\u003c\/p\u003e \u003cp\u003eInterfaces to Many Technologies 162\u003c\/p\u003e \u003cp\u003eProgrammer or Designer Control of Data Placement 163\u003c\/p\u003e \u003cp\u003eParallel Storage and Management of Data 164\u003c\/p\u003e \u003cp\u003eMetadata Management 165\u003c\/p\u003e \u003cp\u003eLanguage Interface 166\u003c\/p\u003e \u003cp\u003eEfficient Loading of Data 166\u003c\/p\u003e \u003cp\u003eEfficient Index Utilization 168\u003c\/p\u003e \u003cp\u003eCompaction of Data 169\u003c\/p\u003e \u003cp\u003eCompound Keys 169\u003c\/p\u003e \u003cp\u003eVariable-Length Data 169\u003c\/p\u003e \u003cp\u003eLock Management 171\u003c\/p\u003e \u003cp\u003eIndex-Only Processing 171\u003c\/p\u003e \u003cp\u003eFast Restore 171\u003c\/p\u003e \u003cp\u003eOther Technological Features 172\u003c\/p\u003e \u003cp\u003eDBMS Types and the Data Warehouse 172\u003c\/p\u003e \u003cp\u003eChanging DBMS Technology 174\u003c\/p\u003e \u003cp\u003eMultidimensional DBMS and the Data Warehouse 175\u003c\/p\u003e \u003cp\u003eData Warehousing across Multiple Storage Media 182\u003c\/p\u003e \u003cp\u003eThe Role of Metadata in the Data Warehouse Environment 182\u003c\/p\u003e \u003cp\u003eContext and Content 185\u003c\/p\u003e \u003cp\u003eThree Types of Contextual Information 186\u003c\/p\u003e \u003cp\u003eCapturing and Managing Contextual Information 187\u003c\/p\u003e \u003cp\u003eLooking at the Past 187\u003c\/p\u003e \u003cp\u003eRefreshing the Data Warehouse 188\u003c\/p\u003e \u003cp\u003eTesting 190\u003c\/p\u003e \u003cp\u003eSummary 191\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 The Distributed Data Warehouse 193\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTypes of Distributed Data Warehouses 193\u003c\/p\u003e \u003cp\u003eLocal and Global Data Warehouses 194\u003c\/p\u003e \u003cp\u003eThe Local Data Warehouse 197\u003c\/p\u003e \u003cp\u003eThe Global Data Warehouse 198\u003c\/p\u003e \u003cp\u003eIntersection of Global and Local Data 201\u003c\/p\u003e \u003cp\u003eRedundancy 206\u003c\/p\u003e \u003cp\u003eAccess of Local and Global Data 207\u003c\/p\u003e \u003cp\u003eThe Technologically Distributed Data Warehouse 211\u003c\/p\u003e \u003cp\u003eThe Independently Evolving Distributed Data Warehouse 213\u003c\/p\u003e \u003cp\u003eThe Nature of the Development Efforts 213\u003c\/p\u003e \u003cp\u003eCompletely Unrelated Warehouses 215\u003c\/p\u003e \u003cp\u003eDistributed Data Warehouse Development 217\u003c\/p\u003e \u003cp\u003eCoordinating Development across Distributed Locations 218\u003c\/p\u003e \u003cp\u003eThe Corporate Data Model — Distributed 219\u003c\/p\u003e \u003cp\u003eMetadata in the Distributed Warehouse 223\u003c\/p\u003e \u003cp\u003eBuilding the Warehouse on Multiple Levels 223\u003c\/p\u003e \u003cp\u003eMultiple Groups Building the Current Level of Detail 226\u003c\/p\u003e \u003cp\u003eDifferent Requirements at Different Levels 228\u003c\/p\u003e \u003cp\u003eOther Types of Detailed Data 232\u003c\/p\u003e \u003cp\u003eMetadata 234\u003c\/p\u003e \u003cp\u003eMultiple Platforms for Common Detail Data 235\u003c\/p\u003e \u003cp\u003eSummary 236\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Executive Information Systems and the Data Warehouse 239\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEIS — The Promise 240\u003c\/p\u003e \u003cp\u003eA Simple Example 240\u003c\/p\u003e \u003cp\u003eDrill-Down Analysis 243\u003c\/p\u003e \u003cp\u003eSupporting the Drill-Down Process 245\u003c\/p\u003e \u003cp\u003eThe Data Warehouse as a Basis for EIS 247\u003c\/p\u003e \u003cp\u003eWhere to Turn 248\u003c\/p\u003e \u003cp\u003eEvent Mapping 251\u003c\/p\u003e \u003cp\u003eDetailed Data and EIS 253\u003c\/p\u003e \u003cp\u003eKeeping Only Summary Data in the EIS 254\u003c\/p\u003e \u003cp\u003eSummary 255\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 External Data and the Data Warehouse 257\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExternal Data in the Data Warehouse 260\u003c\/p\u003e \u003cp\u003eMetadata and External Data 261\u003c\/p\u003e \u003cp\u003eStoring External Data 263\u003c\/p\u003e \u003cp\u003eDifferent Components of External Data 264\u003c\/p\u003e \u003cp\u003eModeling and External Data 265\u003c\/p\u003e \u003cp\u003eSecondary Reports 266\u003c\/p\u003e \u003cp\u003eArchiving External Data 267\u003c\/p\u003e \u003cp\u003eComparing Internal Data to External Data 267\u003c\/p\u003e \u003cp\u003eSummary 268\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Migration to the Architected Environment 269\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA Migration Plan 270\u003c\/p\u003e \u003cp\u003eThe Feedback Loop 278\u003c\/p\u003e \u003cp\u003eStrategic Considerations 280\u003c\/p\u003e \u003cp\u003eMethodology and Migration 283\u003c\/p\u003e \u003cp\u003eA Data-Driven Development Methodology 283\u003c\/p\u003e \u003cp\u003eData-Driven Methodology 286\u003c\/p\u003e \u003cp\u003eSystem Development Life Cycles 286\u003c\/p\u003e \u003cp\u003eA Philosophical Observation 286\u003c\/p\u003e \u003cp\u003eSummary 287\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 The Data Warehouse and the Web 289\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSupporting the eBusiness Environment 299\u003c\/p\u003e \u003cp\u003eMoving Data from the Web to the Data Warehouse 300\u003c\/p\u003e \u003cp\u003eMoving Data from the Data Warehouse to the Web 301\u003c\/p\u003e \u003cp\u003eWeb Support 302\u003c\/p\u003e \u003cp\u003eSummary 302\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Unstructured Data and the Data Warehouse 305\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntegrating the Two Worlds 307\u003c\/p\u003e \u003cp\u003eText — The Common Link 308\u003c\/p\u003e \u003cp\u003eA Fundamental Mismatch 310\u003c\/p\u003e \u003cp\u003eMatching Text across the Environments 310\u003c\/p\u003e \u003cp\u003eA Probabilistic Match 311\u003c\/p\u003e \u003cp\u003eMatching All the Information 312\u003c\/p\u003e \u003cp\u003eA Themed Match 313\u003c\/p\u003e \u003cp\u003eIndustrially Recognized Themes 313\u003c\/p\u003e \u003cp\u003eNaturally Occurring Themes 316\u003c\/p\u003e \u003cp\u003eLinkage through Themes and Themed Words 317\u003c\/p\u003e \u003cp\u003eLinkage through Abstraction and Metadata 318\u003c\/p\u003e \u003cp\u003eA Two-Tiered Data Warehouse 320\u003c\/p\u003e \u003cp\u003eDividing the Unstructured Data Warehouse 321\u003c\/p\u003e \u003cp\u003eDocuments in the Unstructured Data Warehouse 322\u003c\/p\u003e \u003cp\u003eVisualizing Unstructured Data 323\u003c\/p\u003e \u003cp\u003eA Self-Organizing Map (SOM) 324\u003c\/p\u003e \u003cp\u003eThe Unstructured Data Warehouse 325\u003c\/p\u003e \u003cp\u003eVolumes of Data and the Unstructured Data Warehouse 326\u003c\/p\u003e \u003cp\u003eFitting the Two Environments Together 327\u003c\/p\u003e \u003cp\u003eSummary 330\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 The Really Large Data Warehouse 331\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhy the Rapid Growth? 332\u003c\/p\u003e \u003cp\u003eThe Impact of Large Volumes of Data 333\u003c\/p\u003e \u003cp\u003eBasic Data-Management Activities 334\u003c\/p\u003e \u003cp\u003eThe Cost of Storage 335\u003c\/p\u003e \u003cp\u003eThe Real Costs of Storage 336\u003c\/p\u003e \u003cp\u003eThe Usage Pattern of Data in the Face of Large Volumes 336\u003c\/p\u003e \u003cp\u003eA Simple Calculation 337\u003c\/p\u003e \u003cp\u003eTwo Classes of Data 338\u003c\/p\u003e \u003cp\u003eImplications of Separating Data into Two Classes 339\u003c\/p\u003e \u003cp\u003eDisk Storage in the Face of Data Separation 340\u003c\/p\u003e \u003cp\u003eNear-Line Storage 341\u003c\/p\u003e \u003cp\u003eAccess Speed and Disk Storage 342\u003c\/p\u003e \u003cp\u003eArchival Storage 343\u003c\/p\u003e \u003cp\u003eImplications of Transparency 345\u003c\/p\u003e \u003cp\u003eMoving Data from One Environment to Another 346\u003c\/p\u003e \u003cp\u003eThe CMSM Approach 347\u003c\/p\u003e \u003cp\u003eA Data Warehouse Usage Monitor 348\u003c\/p\u003e \u003cp\u003eThe Extension of the Data Warehouse across Different Storage Media 349\u003c\/p\u003e \u003cp\u003eInverting the Data Warehouse 350\u003c\/p\u003e \u003cp\u003eTotal Cost 351\u003c\/p\u003e \u003cp\u003eMaximum Capacity 352\u003c\/p\u003e \u003cp\u003eSummary 354\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13 The Relational and the Multidimensional Models as a Basis for Database Design 357\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Relational Model 357\u003c\/p\u003e \u003cp\u003eThe Multidimensional Model 360\u003c\/p\u003e \u003cp\u003eSnowflake Structures 361\u003c\/p\u003e \u003cp\u003eDifferences between the Models 362\u003c\/p\u003e \u003cp\u003eThe Roots of the Differences 363\u003c\/p\u003e \u003cp\u003eReshaping Relational Data 364\u003c\/p\u003e \u003cp\u003eIndirect Access and Direct Access of Data 365\u003c\/p\u003e \u003cp\u003eServicing Future Unknown Needs 366\u003c\/p\u003e \u003cp\u003eServicing the Need to Change Gracefully 367\u003c\/p\u003e \u003cp\u003eIndependent Data Marts 370\u003c\/p\u003e \u003cp\u003eBuilding Independent Data Marts 371\u003c\/p\u003e \u003cp\u003eSummary 375\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14 Data Warehouse Advanced Topics 377\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEnd-User Requirements and the Data Warehouse 377\u003c\/p\u003e \u003cp\u003eThe Data Warehouse and the Data Model 378\u003c\/p\u003e \u003cp\u003eThe Relational Foundation 378\u003c\/p\u003e \u003cp\u003eThe Data Warehouse and Statistical Processing 379\u003c\/p\u003e \u003cp\u003eResource Contention in the Data Warehouse 380\u003c\/p\u003e \u003cp\u003eThe Exploration Warehouse 380\u003c\/p\u003e \u003cp\u003eThe Data Mining Warehouse 382\u003c\/p\u003e \u003cp\u003eFreezing the Exploration Warehouse 383\u003c\/p\u003e \u003cp\u003eExternal Data and the Exploration Warehouse 384\u003c\/p\u003e \u003cp\u003eData Marts and Data Warehouses in the Same Processor 384\u003c\/p\u003e \u003cp\u003eThe Life Cycle of Data 386\u003c\/p\u003e \u003cp\u003eMapping the Life Cycle to the Data Warehouse Environment 387\u003c\/p\u003e \u003cp\u003eTesting and the Data Warehouse 388\u003c\/p\u003e \u003cp\u003eTracing the Flow of Data through the Data Warehouse 390\u003c\/p\u003e \u003cp\u003eData Velocity in the Data Warehouse 391\u003c\/p\u003e \u003cp\u003e“Pushing” and “Pulling” Data 393\u003c\/p\u003e \u003cp\u003eData Warehouse and the Web-Based eBusiness Environment 393\u003c\/p\u003e \u003cp\u003eThe Interface between the Two Environments 394\u003c\/p\u003e \u003cp\u003eThe Granularity Manager 394\u003c\/p\u003e \u003cp\u003eProfile Records 396\u003c\/p\u003e \u003cp\u003eThe ODS, Profile Records, and Performance 397\u003c\/p\u003e \u003cp\u003eThe Financial Data Warehouse 397\u003c\/p\u003e \u003cp\u003eThe System of Record 399\u003c\/p\u003e \u003cp\u003eA Brief History of Architecture — Evolving to the Corporate Information Factory 402\u003c\/p\u003e \u003cp\u003eEvolving from the CIF 404\u003c\/p\u003e \u003cp\u003eObstacles 406\u003c\/p\u003e \u003cp\u003eCIF — Into the Future 406\u003c\/p\u003e \u003cp\u003eAnalytics 406\u003c\/p\u003e \u003cp\u003eErp\/sap 407\u003c\/p\u003e \u003cp\u003eUnstructured Data 408\u003c\/p\u003e \u003cp\u003eVolumes of Data 409\u003c\/p\u003e \u003cp\u003eSummary 410\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15 Cost-Justification and Return on Investment for a Data Warehouse 413\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCopying the Competition 413\u003c\/p\u003e \u003cp\u003eThe Macro Level of Cost-Justification 414\u003c\/p\u003e \u003cp\u003eA Micro Level Cost-Justification 415\u003c\/p\u003e \u003cp\u003eInformation from the Legacy Environment 418\u003c\/p\u003e \u003cp\u003eThe Cost of New Information 419\u003c\/p\u003e \u003cp\u003eGathering Information with a Data Warehouse 419\u003c\/p\u003e \u003cp\u003eComparing the Costs 420\u003c\/p\u003e \u003cp\u003eBuilding the Data Warehouse 420\u003c\/p\u003e \u003cp\u003eA Complete Picture 421\u003c\/p\u003e \u003cp\u003eInformation Frustration 422\u003c\/p\u003e \u003cp\u003eThe Time Value of Data 422\u003c\/p\u003e \u003cp\u003eThe Speed of Information 423\u003c\/p\u003e \u003cp\u003eIntegrated Information 424\u003c\/p\u003e \u003cp\u003eThe Value of Historical Data 425\u003c\/p\u003e \u003cp\u003eHistorical Data and CRM 426\u003c\/p\u003e \u003cp\u003eSummary 426\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 16 The Data Warehouse and the ODS 429\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eComplementary Structures 430\u003c\/p\u003e \u003cp\u003eUpdates in the ODS 430\u003c\/p\u003e \u003cp\u003eHistorical Data and the ODS 431\u003c\/p\u003e \u003cp\u003eProfile Records 432\u003c\/p\u003e \u003cp\u003eDifferent Classes of ODS 434\u003c\/p\u003e \u003cp\u003eDatabase Design — A Hybrid Approach 435\u003c\/p\u003e \u003cp\u003eDrawn to Proportion 436\u003c\/p\u003e \u003cp\u003eTransaction Integrity in the ODS 437\u003c\/p\u003e \u003cp\u003eTime Slicing the ODS Day 438\u003c\/p\u003e \u003cp\u003eMultiple ODS 439\u003c\/p\u003e \u003cp\u003eODS and the Web Environment 439\u003c\/p\u003e \u003cp\u003eAn Example of an ODS 440\u003c\/p\u003e \u003cp\u003eSummary 441\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 17 Corporate Information Compliance and Data Warehousing 443\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTwo Basic Activities 445\u003c\/p\u003e \u003cp\u003eFinancial Compliance 446\u003c\/p\u003e \u003cp\u003eThe “What” 447\u003c\/p\u003e \u003cp\u003eThe “Why” 449\u003c\/p\u003e \u003cp\u003eAuditing Corporate Communications 452\u003c\/p\u003e \u003cp\u003eSummary 454\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 18 The End-User Community 457\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Farmer 458\u003c\/p\u003e \u003cp\u003eThe Explorer 458\u003c\/p\u003e \u003cp\u003eThe Miner 459\u003c\/p\u003e \u003cp\u003eThe Tourist 459\u003c\/p\u003e \u003cp\u003eThe Community 459\u003c\/p\u003e \u003cp\u003eDifferent Types of Data 460\u003c\/p\u003e \u003cp\u003eCost-Justification and ROI Analysis 461\u003c\/p\u003e \u003cp\u003eSummary 462\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 19 Data Warehouse Design Review Checklist 463\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhen to Do a Design Review 464\u003c\/p\u003e \u003cp\u003eWho Should Be in the Design Review? 465\u003c\/p\u003e \u003cp\u003eWhat Should the Agenda Be? 465\u003c\/p\u003e \u003cp\u003eThe Results 465\u003c\/p\u003e \u003cp\u003eAdministering the Review 466\u003c\/p\u003e \u003cp\u003eA Typical Data Warehouse Design Review 466\u003c\/p\u003e \u003cp\u003eSummary 488\u003c\/p\u003e \u003cp\u003eGlossary 489\u003c\/p\u003e \u003cp\u003eReferences 507\u003c\/p\u003e \u003cp\u003eArticles 507\u003c\/p\u003e \u003cp\u003eBooks 510\u003c\/p\u003e \u003cp\u003eWhite Papers 512\u003c\/p\u003e \u003cp\u003eIndex 517\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eWilliam H. Inmon\u003c\/b\u003e is the acknowledged \"Father of Data Warehousing\" and a partner in www.billinmon.com, a Web site featuring information on data warehousing and related technologies. He has written more than 40 books on database and data warehousing technologies, and is a frequent speaker (and often the keynote) at major conferences.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eThe most comprehensive introduction to the core concepts and methods of data warehousing\u003c\/b\u003e  \u003c\/p\u003e\u003cp\u003eData warehouses provide a much-needed strategy for organizations to collect, store, and analyze vast amounts of business data. As businesses expand both brick-and-mortar and online activities, the field of data warehousing has become increasingly important. Since it was first published in 1990, W. H. Inmon's \u003ci\u003e Building the Data Warehouse\u003c\/i\u003e  has been the bible of data warehousing— it is the book that launched the data warehousing industry and it remains the preeminent introduction to the subject. This new edition covers the latest developments with this technology, many of which have been pioneered by Inmon himself. \u003c\/p\u003e\u003cp\u003eAn overview of all the fundamental components of data warehouse systems offers you a refresher of the methods used for data warehouse design; various data warehouse migration strategies; and the technologies that can be applied for loading, indexing, and managing data. In order to bring you up to date, this in-depth guide then features the latest advances in data warehousing. \u003c\/p\u003e\u003cp\u003eNew chapters cover: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eUnderstanding methods for handling unstructured data in a data warehouse\u003c\/li\u003e \u003cli\u003eStoring data across multiple storage media\u003c\/li\u003e \u003cli\u003eExamining the pros and cons of relational vs. multidimensional design\u003c\/li\u003e \u003cli\u003eMeasuring return on investment in planning data warehouse projects\u003c\/li\u003e \u003cli\u003eExploring advanced topics, including data monitoring and testing\u003c\/li\u003e \u003c\/ul\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988867563749,"sku":"NP9780764599446","price":58.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780764599446.jpg?v=1761781842","url":"https:\/\/k12savings.com\/products\/building-the-data-warehouse-isbn-9780764599446","provider":"K12savings","version":"1.0","type":"link"}