Data Architecture

Data is an expensive and expansive asset. Informatio hunger has forced retention capacity from megabytes to terabytes of data. Soon there will be Exabytes, and then Zetabytes. There is a constant search for space for the billions of bytes of data we need to perform our information processing. Millions of dollars are spent accumulating data, and millions more are paid to the professional staff that nurture, husband, and secure it. Additional millions are paid to the professionals that tease information out of theses billions of bytes of data. Billions of dollars are being spent for this asset; it would seem prudent to design the structures that hold it is such a manner as to be flexible and responsive to change as well as readily available for information access. This book explains the principles underlying data architecture, how data evolves with organizations, and the challenges organizations face in structuring and managing data, and it discusses proven methods and technologies to solve the complex issues dealing with data. The author takes a holistic approach to the field of data architecture various applied perspectives, including data modeling, data quality, enterprise information management, database design, data warehousing, and data governance. Zusammenfassung Data is an expensive and expansive asset. Information capture has forced storage capacity from megabytes to terabytes! exabytes and! pretty soon! zetabytes of data. This title explains the principles underlying data architecture! how data evolves with organizations! and the challenges organizations face in structuring and managing their data.

Zusammenfassung
Data is an expensive and expansive asset. Information capture has forced storage capacity from megabytes to terabytes, exabytes and, pretty soon, zetabytes of data. This title explains the principles underlying data architecture, how data evolves with organizations, and the challenges organizations face in structuring and managing their data.

Inhalt

Section One: The Principles1: Understanding Architectural Principles2: Enterprise Architecture Frameworks and Methodologies3. Enterprise Level Data Architecture Practices4: Understanding Development Methodologies

Section Two: The Problem5: Business Evolution6 Business Organizations7. Productivity inside the Data Organization8. Solutions That Cause Problems

Section Three: The Process9. Data Organization Practices10. Models and Model Repositories11. Model Constructs and Model Types12. Time as a Dimension of the Database13. Concepts of Clustering, Indexing and Structures

Section Four: The Product14. Basic Requirements for Physical Design15. Physical Database Considerations16. Interpretation of Models

Section Five: Specialized Databases17. Data Warehouses I18. Data Warehouses II19. Dimensional Warehouses from Enterprise Models20. The Enterprise Data Warehouse21. Object and Object/Relational Databases22. Distributed Databases