TDWI Advanced Data Modeling Techniques Training in South Africa

Whether you are a business data modeler who represents data requirements as entities and relationships, or a physical data modeler more concerned with tables, columns, and indexes, you know that the hard stuff lies beneath the surface. Every data design, whether logical or technical, is challenged by one or more complex considerations—scalability, adaptability, performance, legacy and package databases, etc. Every data model raises questions. Advanced modeling techniques provide many of the answers. This course explores different situations facing data modeling practitioners and provides information and techniques to help them develop the appropriate data models.

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