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