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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.

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dimensional modeling primer

Dimensional Modeling Primer

Chapter 1

Kimball & Ross

concepts discussed
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
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
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
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
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
Components of a Data Warehouse
  • Operational source systems
  • Data staging area
  • Data presentation area
  • Data access tools
data staging area
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 area9
Data Staging Area
  • Dominated by simple activities sorting and sequential processing.
  • Normalized data is acceptable, although this is not the end goal.
data presentation
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 presentation11
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
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
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
Additional Considerations
  • Meta data
  • Operational data store