| 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 |
J.D. WELCH
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. |
