Dimensional Modeling Primer

Chapter 1

Kimball & Ross

Concepts Discussed

Business driven goals Data warehouse publishing Major components Importance of dimensional modeling for the

presentation area Facts & dimension tables Myths of dimensional modeling Pitfalls to avoid

Different Information Worlds

Users of operational system turn the wheels of an organization

Users of data warehouse watch the wheels of the organization turn

Warehouse users have drastically different needs than users of operational systems

Returning Themes

We have mountains of data but we cannot access it

We need to slice the data in different ways Need to make it easy for business users to access

the data Just show me what is important It drives me craze when different people present

the same metrics with different numbers Fact-based decision making

Goals of Data Warehouse

Make an organization’s information easily accessible

Present the information in a consistent manner Adaptive and resilient to change Secure and protects information Serves as a foundation for improved decision

making Business users must accept the data warehouse if

it is to be useful

Publishing Metaphor

Data warehouse manager is a “publisher” of the right data

Responsible for publishing data collected from a variety of sources and edited for quality and consistency

Components of a Data Warehouse Operational source systems Data staging area Data presentation area Data access tools

Data Staging Area

Key structural requirement is that is it off-limits to business users and does not provide query and presentation services.– Correct misspellings, resolve domain conflicts,

deal with missing elements, parse into standard formats, combine data from multiple sources.

– Normalized structures sometimes called “enterprise data warehouse” – it is a misnomer (Kimball).

Data Staging Area

Dominated by simple activities sorting and sequential processing.

Normalized data is acceptable, although this is not the end goal.

Data Presentation

Series of integrated data marts. Data mart is data from a single business process. Wedge of the overall pie.

Data must be presented, stored and accessed in dimensional schema.

Data Presentation

Should not be in normalized form. They must contain detailed atomic data in

addition to data in summary form, because the queries are ad hoc and cannot be predicted.

Facts and dimensions – called conformed.

Presentation Area

If it is based on a relational data base, it is called start schema.

If it is multidimensional database, or OLAP, then the data is stored in cubes.

Data Access Tools

Querying is the whole point of DW. Can be as simple as an ad hoc query tool or

as complex as a data mining or a modeling application.

Parameter driven analytic operations. 80 to 90 of the users are served by canned

applications.

Additional Considerations

Meta data Operational data store