The Essential Guide to Data Warehousing

Abstract

I. FUNDAMENTAL COMMITMENTS. 1. Basic Data Warehousing Distinctions. An Architecture, Not A Product. The One Fundamental Question. The One Question-The Thousand and One Answers.... The First Distinction: Transaction and Decision Support System. Data Warehouse Sources of Data. Dimensions. The Data Warehouse Fact. The Data Warehouse Model of the Business: Alignment. The Data Cube. Aggregation. Data Warehouse Professional Roles. The Data Warehouse Process Model. Summary. 2. A Short History of Data. In the Beginning.... Fast Forward to Modern Times. The Very Idea of Decision Support. From Mainframes To PCs. The Promise of the Relational Database. Data Every Which Way. From Client-Server to Thin Client Computing. Why Will Things Be Different This Time? The More Things Change, the More They Stay the Same. Model of Technology Dynamics. Summary. 3. Justifying Data Warehousing. Competition for Limited Resources. An Integrated Business and Technology Solution. Economic Value, Not Business Benefits. Selling the Data Warehouse. The Reporting Data Warehouse: Running Fewer Errands. The Supply Chain Warehouse. The Cross-Selling Warehouse. The Total Quality Management Data Warehouse. The Profitability Warehouse. Data Warehousing Case Vignettes in the Press. Summary. 4. Data Warehousing Project Management. Simulating a Rational Design Process. Managing Project Requirements. Managing the Development of Architecture. Managing Project Schedule. Managing Project Quality. Managing Project Risks. Managing Project Documentation. Managing the Project Development Team. Managing Project Management. Summary. II. DESIGN AND CONSTRUCTION. 5. Business Design: The Unified Representations of The Customer and Product. The Critical Path: Alignment. A Unified Representation of the Customer. Data Scrubbing. The Cross-Functional Team. Hierarchical Structure. Customer Demographics. A Unified Representation of the Product. Data Marts: Between Prototype and Retrotype. Summary. 6. Total Data Warehouse Quality. The Information Product. Data Quality as Data Integrity. Intrinsic Qualities. Ambiguity. Timeliness and Consistency in Time. Security. Secondary Qualities. Credibility. Quality Data, Quality Reports. Information Quality, System Quality. Performance. Availability. Scalability. Functionality. Maintainability. Reinterpreting the Past. Summary. 7. Data Warehousing Technical Design. Use case Scenarios. Abstract Data Types and Concrete Data Dimensions. Data Normalization: Relevance and Limitations. Dimensions and Facts. Primary and Foreign Keys. Design for Performance: Technical Interlude. Summary. 8. Data Warehouse Construction Technologies: SQL. The Relational Database: A Dominant Design. Twelve Principles. Thinking in Sets: Declarative and Procedural Approaches. Data Definition Language. Indexing: B-Tree. Indexing: Hashing. Indexing: Bitmap. Indexing Rules of Thumb. Data Manipulation Language. Data Control Language. Stored Procedures. User-Defined Functions. Summary. 9. Data Warehouse Construction Technologies: Transaction Management. The Case For Transaction Management: The ACID Test. The Logical Unit of Work. Two-tier and Three-tier Architectures. Distributed Architecture. Middleware: Remote Procedure Call Model. Middleware: Message-Oriented Middleware. The Long Transaction. Summary. III. OPERATIONS AND TRANSFORMATIONS. 10. Data Warehouse Operation Technologies: Data Management. Database Administration. Backing Up the Data (in the Ever-Narrowing Backup Window). Recovering the Database: Crash Recovery. Recovering the Database: Version (Point-in-Time) Recovery. Recovering the Database: Roll-Forward Recovery. Managing Lots of Data: Acres of Disk. Managing Lots of Data: System-Controlled Storage. Managing Lots of Data: Automated Tape Robots. RAID Configurations. Summary. 11. Data Warehousing Performance. Performance Parameters. Denormalization for Performance. Aggregation For Performance. Buffering For Performance. Partitioning For Performance. Parallel Processing: Sh