The relationship between tables and dimensions

The relationship between tables and dimensions

How the dimensions are defined to the OLAP tools is a different issue from how they are physically implemented as tables in a snowflake schema.

I've reviewed a number of dimensional models and I often find a one-to-one relationship between physical tables and the dimensions that are defined to the OLAP tool, even in snowflake schemas. In most cases I find clients imposing the constraints of star schemas upon their snowflake schemas.

A good example of this is geography. The classic geography hierarchy (i.e., zip-city-county-state-region-country) is interesting to a variety of dimensions -- store, customer, supplier and so on, but redundant tables are unnecessary.

If the tables zip, city, county, state, region, country, etc. are maintained in one place and defined to the OLAP tool as simply higher levels in the hierarchies of store, customer and supplier, etc., there is only one physical place where changes to these items are stored and maintained.

The store dimension hierarchies then become store-zip-city-county-state-region-country. Users can always query for dimension values at the lowest level of the hierarchy so this dimension could be used for looking up sales (fact) by store as well as sales by zip, sales by city, etc. Geography is seldom a pure dimension in and of itself.

Star schemas would physically force one to keep the geographical data updated in each dimension it is found in, such as geographical data

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in store, customer and supplier. If you're doing snowflake schemas, reap the benefits and reuse tables in multiple logical dimensions!

For more information, check out searchCRM's Best Web Links on Data Warehousing.


This was first published in November 2001

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