TDWI Advanced Data Modeling Techniques Training in Denmark

Data modelers with some practical experience; data architects; database developers

• Enterprise architecture approaches and how to apply them
• How big data and analytics impact traditional approaches
• Different data models and how they relate to each other
• The role of modeling in analytics
• Higher normalization forms
• How to effectively apply generalization and specialization
• The role of metadata management in data governance
• State and time dependencies and how to handle them
• How to validate the data model
• How to transform the business data model into physical models based on the application
• The implications of alternative storage approaches
• The roles and structures of complementary models
• How to deal with multiple time zones and currencies

Module 1: Data Modeling Concepts

• Enterprise Architecture
  • Definition
  • Zachman Framework Overview
  • Data Modeling Framework for BI
  • Levels of Data Models – Enterprise Perspective
  • Levels of Data Models – Project Perspective
  • The Open Group Architecture Framework
  • Control Objectives for Information Technology
  • Frameworks – Discussion
• Higher Normal Forms
  • Boyce-Codd Normal Form
  • Fourth Normal Form
  • Fifth Normal Form
  • Anchor Modeling
  • Data Vault Modeling
• Specialization and Generalization
  • Roles and Classifications
  • Considerations
  • Party
• Presentation
  • Standards

Module 2: Business Data Model Development

• Business Data Model Development Approaches
  • Top-Down
  • Bottom-Up
  • Generic Models
  • Limited Depth Models
• Data Modeling Roles
  • Functions, Traits, and Challenges
• Business Data Model Application
  • Basis for System Data Model
  • Transformation and Integration Foundation
  • Package Selection
  • Business Communications
  • Data Profiling
  • Data Governance
• Data Governance
  • Definition
  • Quality Improvement
  • Real-Time Implications
  • Metadata Management
  • Information Subject Area
  • Big Data
  • Big Data Challenges

Module 3: System and Physical Data Model Development

• Data Modeling Roles
  • Functions, Traits, and Challenges
• Globalization / Localization
  • Information Needs
  • Currencies
  • Time Zones
  • Languages
• Non-Relational Data Structures
  • Columnar Databases
  • In-Memory Databases
  • XML Structures
  • Key Value Pairs
• Business Analytics
  • Definition
  • Schema on Read
  • Modeling Process

Module 4: Additional Concepts

• Recursive Relationships
  • Normalized Approach
  • Dimensional Approach
• Cloud
  • Modeling Implications
• Complementary Models
  • State Transition Model
  • Function Models
  • Process Models
  • Model Management
• Model Management
  • Model Validation and Testing
  • Model Synchronization
  • Tool Exploitation
  • Data Modeling Tools
  • Repositories

Module 5: Summary and Conclusions

• Summary of Key Points
  • A Quick Review
• Appendix A: Bibliography and References
• Appendix B: Exercises
  • Exercise 1: Normalization to Higher Normal Forms
  • Exercise 2: Party Modeling
  • Exercise 3: Financial Institution Model
  • Exercise 4: Model Application for Data Profiling
  • Exercise 5: Application System Model Development
  • Exercise 6: Model Evaluation