Data Warehouse Data Modeling

Data Warehouse Data Modeling: The Key to Business Intelligence Success

This in-depth course covers all aspects of data modeling for a data warehouse - from the need for a subject area diagram and logical data model, to the design steps used to create the data warehouse model and data mart models. The different types of decision support models – normalized, star schema, snowflake, etc., are covered in detail. In addition, hands-on workshops are provided to help the students get “real world” experience translating the end users’ requirements into the physical models needed to support their decision support environments

Objectives

Understand the Corporate Information Factory and its component parts
Understand the differences among the models and where each model is appropriate
The enterprise, fully normalized model
The data warehouse data model
The data mart models
Understand the methodology for gathering end user requirements
Be able to transform the corporate data model into the various decision support models
Data warehouse physical model
Data mart physical models
Understand the need for capturing metadata and the types of metadata that are critical to the success of the environment
Understand the issues and architecture necessary to implement the models
Understand how and where to use many of the tools available.

Duration 3 Days

Prerequisite Logical data modeling

Optional Prerequisite
Knowledge of database design, particularly for relational databases and of the data warehouse

Course Format Lecture and workshop

Instructor Claudia Imhoff or Jonathan Geiger