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, and more. 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.
You Will Learn
- 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
Audience
Data modelers with some practical experience
Data architects
Database developers
Prerequisite
This course assumes completion of the course TDWI Data Modeling: Data Analysis and Design for BI and Data Warehousing Systems or equivalent understanding of entity-relationship modeling, dimensional modeling, and DW terms and concepts.