Dimensional Modeling Primer Chapter 1 Kimball & Ross.
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Transcript of Dimensional Modeling Primer Chapter 1 Kimball & Ross.
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