Data Warehousing : architecture and implementation / by Mark Humphries, Michael W. Hawkins and Michelle C. Dv

Index included

I. INTRODUCTION. 1. The Enterprise IT Architecture. The Past: Evolution of Enterprise Architectures. The Present: The IT Professional's Responsibility. Business Perspective. Technology Perspective. Architecture Migration Scenarios. Migration Strategy: How Do We Move Forward? 2. Data Warehouse Concepts. Gradual Changes in Computing Focus. The Data Warehouse Defined. The Dynamic, Ad Hoc Report. The Purposes of a Data Warehouse. A Word about Data Marts. A Word about Operational Data Stores. Data Warehouse Cost-Benefit Analysis / Return On Investment. II. PEOPLE. 3. The Project Sponsor. How Will a Data Warehouse Affect our Decision-Making Processes? How Does a Data Warehouse Improve My Financial Processes? Marketing? Operations? When Is a Data Warehouse Project Justified? What Expenses Are Involved? What Are the Risks? Risk-Mitigating Approaches. Is My Organization Ready for a Data Warehouse? How Do I Measure the Results? 4. The CIO. How Do I Support the Data Warehouse? How Will My Data Warehouse Evolve? Who Should Be Involved in a Data Warehouse Project? What Is the Team Structure Like? What New Skills Will My People Need? How Does Data Warehousing Fit into My IT Architecture? How Many Vendors Do I Need to Talk To? What Should I Look for in a Data Warehouse Vendor? How Does Data Warehousing Affect My Existing Systems? Data Warehousing and its Impact on Other Enterprise Initiatives. When Is a Data Warehouse Not Appropriate? How Do I Manage or Control a Data Warehouse Initiative? 5. The Project Manager. How Do I Roll Out a Data Warehouse Initiative? How Important Is the Hardware Platform? What Technologies Are Involved? Do I Still Use Relational Databases for Data Warehousing? How Long Does a Data Warehousing Project Last? How Is a Data Warehouse Different from Other IT Projects? What Are the Critical Success Factors of a Data Warehousing Project? III. PROCESS. 6. Warehousing Strategy. Strategy Components. Determine Organizational Context. Conduct Preliminary Survey of Requirements. Conduct Preliminary Source System Audit. Identify External Data Sources (If Applicable). Define Warehouse Rollouts (Phased Implementation). Define Preliminary Data Warehouse Architecture. Evaluate Development and Production Environments and Tools. 7. Warehouse Management and Support Processes. Define Issue Tracking and Resolution Process. Perform Capacity Planning. Define Warehouse Purging Rules. Define Security Measures. Define Backup and Recovery Strategy. Set Up Collection of Warehouse Usage Statistics. 8. Data Warehouse Planning. Assemble and Orient Team. Conduct Decisional Requirements Analysis. Conduct Decisional Source System Audit. Design Logical and Physical Warehouse Schema. Produce Source-to-Target Field Mapping. Select Development and Production Environment and Tools. Create Prototype for This Rollout. Create Implementation Plan for this Rollout. Warehouse Planning Tips and Caveats. 9. Data Warehouse Implementation. Acquire and Set Up Development Environment. Obtain Copies of Operational Tables. Finalize Physical Warehouse Schema Design. Build or Configure Extraction and Transformation Subsystems. Build or Configure Data Quality Subsystem. Build Warehouse Load Subsystem. Set-up Data Warehouse Schema. Set Up Data Warehouse Metadata. Set Up Data Access and Retrieval Tools. Perform the Production Warehouse Load. Conduct User Training. Conduct User Testing and Acceptance. IV. TECHNOLOGY. 10. Hardware and Operating Systems. Parallel Hardware Technology. Hardware Selection Criteria. 11. Warehousing Software. Overview. Middleware and Connectivity Tools. Extraction Tools. Transformation Tools. Data Quality Tools. Data Loaders. Database Management Systems. Metadata Repository. Data Access and Retrieval Tools. Data Modeling Tools. Warehouse Management Tools. Source Systems. 12. Warehouse Schema Design. OLTP Systems Use Normalized Data Structures. Dimensional Modeling for Decisional Systems. Two Types of Tables: Facts and Dimensions. A Schema Is a Fact Table and Its Related Dimension Tables. Facts Are Fully Normalized, Dimensions Are Denormalized. Dimensional Hierarchies and Hierarchical Drilling. The Time Dimension. The Grain of the Fact Table. The Fact Table Key Is the Concatenation of Dimension Keys. Aggregates or Summaries. Dimensional Attributes. Multiple Star Schemas. Core and Custom Tables. 13. Warehouse Metadata. Metadata Are a Form of Abstraction. Why Are Metadata Important? Metadata Types. Versioning. Metadata as the Basis for Automating Warehousing Tasks. 14. Warehousing Applications. The Early Adoptors. Types of Warehousing Applications. Specialized Applications of Warehousing Technology. V. WHERE TO NOW? 15. Warehouse Maintenance and Evolution. Regular Warehouse Loads. Warehouse Statistics Collection. Warehouse User Profiles. Security and Access Profiles. Data Quality. Data Growth. Updates to Warehouse Subsystems. Database Optimization and Tuning. Data Warehouse Staffing. Warehouse Staff and User Training. Subsequent Warehouse Rollouts. Chargeback Schemes. Disaster Recovery. 16. Warehousing Trends. Continued Growth of the Data Warehouse Industry. Increased Adoption of Warehousing Technology by More Industries. Increased Maturity of Data Mining Technologies. Emergence and Use of Metadata Interchange Standards. Increased Availability of Web-Enabled Solutions. Popularity of Windows NT for Data Mart Projects. Availability of Warehousing Modules for Application Packages. More Mergers and Acquisitions Among Warehouse Players. VI. Appendices. Appendix A. R/olapXLA A User,s Guide. Appendix B. Warehouse DesignerA A User's Manual. Appendix C. Online Data Warehousing Resources. Appendix D. Tool and Vendor Inventory. Appendix E. Software License Agreement. VII. REFERENCES & FURTHER READING.

This book delivers what every data warehousing project participant needs most: a thorough overview of today's best solutions, and a reliable step-by-step process for building warehouses that meet their objectives. It answers the key questions asked by everyone involved in a data warehouse initiative: project sponsors, developers, managers, and CIOs. And, with over 70 figures, it doesn't just tell you how to get the