Data Architecture: From Zen to Reality explains the principles underlying data architecture, how data evolves with organizations, and the challenges organizations face in structuring and managing their data. Using a holistic approach to the field of data architecture, the book describes proven methods and technologies to solve the complex issues dealing with data. It covers the various applied areas of data, including data modelling and data model management, data quality, data governance, enterprise information management, database design, data warehousing, and warehouse design. This text is a core resource for anyone customizing or aligning data management systems, taking the Zen-like idea of data architecture to an attainable reality.
The book presents fundamental concepts of enterprise architecture with definitions and real-world applications and scenarios. It teaches data managers and planners about the challenges of building a data architecture roadmap, structuring the right team, and building a long term set of solutions. It includes the detail needed to illustrate how the fundamental principles are used in current business practice. The book is divided into five sections, one of which addresses the software-application development process, defining tools, techniques, and methods that ensure repeatable results.
Data Architecture is intended for people in business management involved with corporate data issues and information technology decisions, ranging from data architects to IT consultants, IT auditors, and data administrators. It is also an ideal reference tool for those in a higher-level education process involved in data or information technology management.
Section One: The Principles 1: Understanding Architectural Principles 2: Enterprise Architecture Frameworks and Methodologies 3. Enterprise Level Data Architecture Practices 4: Understanding Development Methodologies
Section Two: The Problem 5: Business Evolution 6 Business Organizations 7. Productivity inside the Data Organization 8. Solutions That Cause Problems
Section Three: The Process 9. Data Organization Practices 10. Models and Model Repositories 11. Model Constructs and Model Types 12. Time as a Dimension of the Database 13. Concepts of Clustering, Indexing and Structures
Section Four: The Product 14. Basic Requirements for Physical Design 15. Physical Database Considerations 16. Interpretation of Models
Section Five: Specialized Databases 17. Data Warehouses I 18. Data Warehouses II 19. Dimensional Warehouses from Enterprise Models 20. The Enterprise Data Warehouse 21. Object and Object/Relational Databases 22. Distributed Databases