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Enterprise Data Warehouse, Volume I, The: Planning, Building and Implementation, 1/e
Eric Sperley
Hewlett-Packard
Published April, 1999 by Prentice Hall PTR (ECS Professional)
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Nuts-and-bolts data warehousing techniques that get the job done!
Finally, specifics! This isn't a "theory" book: it's an in-the-trenches, step-by-step guide to deploying data warehouses that align tightly with your business objectives. From Joint Application Development (JAD) techniques that maximize bang for the buck, to choosing the best hardware, software, and end-user access components, Eric Sperley delivers field-tested techniques you can rely on. Coverage includes:
Sperley delivers a practical, business-focused methodology that's flexible enough for any enterprise - and so detailed it'll never leave you wondering what to do next. If your data warehouse must deliver sustainable competitive advantage, don't settle for anything less.
ERIC SPERLEY is a System Engineering Consultant for EMC specializing in Data Warehousing. Formerly a Technical Consultant with Hewlett-Packard, he has over 15 years' experience in database consulting and research and has conducted several training courses in Data Analysis for Motorola, 3M, and Software Publishing Corporation.
(NOTE: Each chapter begins with an Introduction and concludes with
Questions and Projects.)
Preface.
1. Brief History of Information Technology.
History of IT.
Silos of Business Information.
What a Data Warehouse Is. Answering Business Questions. The
Enterprise Data Model. Methodology Outline: Scope, Pilot, Production.
Spiral Process. Rapid Application Development.
Data Warehouse Architecture. Information Worker Access.
2. Business and IT Alignment for the Data Warehouse.
Development Tutorial. Business Process Reengineering. Business and
IT Alignment.
Positional Assessment. Capabilities Position. Situational
Assessment. Value Chain Assessment.
The Flexible IT Department. Open System. ROI and Justification. IT
Service Management.
3. How to Plan and Build a Data Warehouse.
Business Needs Analysis. IT Readiness Evaluation. Project Selection.
Warehouse Conceptual Architecture. Warehouse Logical Architecture. Warehouse
Physical Architecture. Data Architecture. Implementation.
4. Project Selection and Scope.
Business Requirements Discovery: Executive Interviews. Business
Requirements Definition: JAD Sessions. Scoping and Estimation. Planning.
Define the Project. Plan the Project. Manage the Project.
Team Members and Skill Sets.
5. Data Modeling.
Entities.
Enterprise Data Modeling.
Just-Enough Enterprise Data Model.
Star Schema Analysis: Creating the Dimensional Model.
Model Development Methods. Granularity. Time. Events.
Developing the Dimensional Model. Snowflakes. Physical Modeling. Ten
Commandments of Dimensional Data Modeling. Two Faces of the Pyramid:
Transactional ER and Analytical Star.
6. The Metadata Repository.
Introduction: What is Metadata? The Metadata Usage Model.
Implementation—Time Metadata. Active Run-Time Metadata. Passive
Run-Time Metadata.
The Metadata Dimensions Model.
Activities Metadata. Locations Metadata. Entities Metadata. People
Metadata. Motivation Metadata. Time Metadata. Metadata Capture and
Maintenance. Initial Metadata Creation. Large Warehouse or Multiple-Subject-
Area.
The Information Users' Guide.
7. Achieving Quality Information in the Data Warehouse.
The Value of Quality Information. Difficulty of Obtaining Quality
Data. Methods to Evaluate the Value of Quality Data. For What Quality Should
We Strive? Methods to Evaluate the Data. Tools to Evaluate the Data. The Data
Evaluation or Audit.
8. The Conceptual and Logical Data Warehouse.
Why Principle-Centered? Metaprinciples: Principles About the
Principles. The Principles.
General Principles. Data Principles. Query Principles. Working Store
Principles. Metadata Principles. Scalability Principles. Warehouse Management
Principles. Architectural Principles and the Zachman Framework. Architectural
Principles Summary.
Conceptual Models.
Unplanned Decision Support. Virtual Data Warehouse. Semantic
Integration of Subject Areas. Query Managed Subject Areas. Monolithic
Warehouse. Standard Data Archive.
Architecture Selection.
Unplanned Decision Support. Virtual Data Warehouse. Semantic
Integration of Subject Areas. Query Managed Subject Areas. Monolithic
Warehouse. Standard Data Archive.
Logical Models.
9. The Physical Data Warehouse.
Physical Storage. Database Considerations. The Database Server
Hardware. Operating Systems.
Performance. Resilience. Integration. Security. Manageability.
The Query Server and the Application Server. Networks and
Connectivity. Middleware.
Usage Tracker. Intelligent Warehouse. Transaction Processing
Monitors. Middleware Selection.
Knowledge Engineering Workstations. Deploying the Architecture.
10. Data Transformation.
Planning. Data Extraction and Movement Methods. Data Transformation.
Data Loading.
11. Data Access.
Tool Selection.
Information Consumer Types. All That FLAP. Vendor Selection
Criterion.
Information Distribution. Web Access. Spreadsheets. Visualization
Tools. Query Tools.
Technical Functionality. Query Functionality. Presentation
Functionality. Interface Functionality.
EIS and DSS Tool Types.
Query Management.
Data Mining Introduction.
12. Data Mining.
Data Preparation. Neural Networks. The Genetic Algorithm.
Clustering and Classification. Decision Trees. Statistics.
Regression Modeling. Discriminant Analysis.
Software Products. Software Example.
Glossary.
Bibliography.
References.
Index.
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