Central Time CT

Prerequisite: This course assumes basic understanding of data warehousing fundamentals. It also assumes some basic understanding of entity-relationship modeling.

Mark Peco

CBIP

Analytics Consultant and Instructor

Course Outline

This data modeling techniques course explores different situations facing data modeling practitioners and provides information and techniques to help them develop the appropriate data models.

BI and analytics systems challenge the proven data modeling techniques of the past. The expanding scope of data management technologies, consumption demands, and data science processes require updated modeling skills. The data modeler’s toolbox must address relational data, NoSQL data, dimensional data, and master data. Modeling processes must include both top-down requirements-driven and bottom-up discovery methods. For those with data modeling experience, this course extends their skills to meet today’s modeling challenges. Those new to data modeling are introduced to the broad range of modeling skills needed for BI and data warehousing systems. Those who need to understand data models, but not necessarily to develop them, will learn about the various forms of models and what they are intended to communicate.

Register today for TDWI's course on data analysis and design for BI and analytics solutions.

You Will Learn

• Differences in modeling techniques for business transactions, business events, and business metrics
• Different types of data and their implications
• How modeling processes differ based on analytics objectives and data management technology
• Application of business context to modeling activities
• The role of business requirements in BI data modeling
• The role of source data analysis in data modeling
• Use of normalized modeling techniques for data warehouse analysis and design
• Use of dimensional modeling techniques for BI and data mart analysis and design
• The roles of generalization and abstraction in data warehouse design
• The roles of identity and hierarchy management in data model design
• How time-variant data is represented in data models
• Implementation and optimization considerations for data stores

Geared To

• Data architects
• Data modelers
• BI program and project managers
• BI and data warehousing systems developers