Prerequisite: DW fundamentals helpful

This course encapsulated years of strategic methodology and modeling experience and techniques in two days. This course will give you the ability to learn powerful and important methodology and modeling techniques. After this course, you will be able to use many of the tips, techniques and knowledge at your company or at your clients’. This course will establish some foundations and quickly go into detail on methodology and modeling.

Methodology, Data Modeling and Design 
for the Data Warehouse, Part II
Tuesday, April 28, 
10.45 - 17.00
Data Modeling and design for the data warehouse

A Review of Data Modeling Terms and Techniques
• The Conceptual Data Model
• The Logical Data Model
• The Physical Data Model
• Entities, Relationships, Attributes and Identifiers
• Normalization
• Tables, Joins, Columns and Keys
• Symbols

Transforming an Operational Data Model into an Informational Model
• Eliminating Purely Operational Data Elements

• Adding the Time Component to Each Entity
• Managed Redundancy
• Identifying Derived Data Elements
• Transforming Attributes
• Artifacting
• Denormalization
• Granular and Derived Data in the Same Level of the Data Warehouse
• Data Volatility Issues

Data Access Considerations
• Bottom-Up vs. Top-Down Design
• Access vs. Analysis
• Query and Reporting Tools
• OLAP Tools
• Statistical Analysis and Other Complex Tools


Creating the Atomic Level Physical Data Model
The foundation for the data warehouse is the atomic, or organizationally structured, level of data and serves as the single, integrated foundation that addresses all of the informational processing requirements of the organization. This section will be a workshop where an operational data model will be transformed into an informational model using the techniques described in the previous section.

Multidimensional Design in the Data Warehouse Architecture

Multidimensional Analysis OLAP, MOLAP and ROLAP 
The Star Schema


DataWing Consulting Services, LLC 
J.D. Welch is an expert on solving the real-world problems that arise during a data warehousing project, and advises clients on all aspects of implementing data warehouses, including conducting training courses, developing project plans, developing logical and physical data models, and reviewing completed implementations. He was one of the first practitioners to design, develop and implement an architected data warehouse. He managed the implementation of several data warehouse projects for the sellular telephone industry from 1984 through 1993 and has also helped Ralston Purina, Citibank and Southwestern Bell Mobile Systems with their projects.
"The course covered a lot of key issues and makes clear things you should be aware of when you are building a data warehouse. Great 
level of information for beginners like me." Isabel Cavazos, Cemtec