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| Version | User | Scope of changes |
|---|---|---|
| Jan 21 2008, 11:29 AM EST (current) | keith_laker | 206 words added, 2 photos added |
| Jan 12 2008, 10:17 AM EST | keith_laker |
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The aim of this page is to provide a glossary of common OLAP and data warehousing terms and expressions. This list is for all terms beginning with f
The image above illustrates a common example of asalesfact table and dimension tablescustomers,products,promotions,times, andchannels.
Snowflake schemas normalize dimensions to eliminate redundancy.The image abovepresents a graphical representation of a fact table within snowflake schema.
Fact
See MeasureFact Table
A fact table typically has two types of columns: those that contain numeric facts (often called measurements), and those that are foreign keys to dimension tables. A fact table contains either detail-level facts or facts that have been aggregated. Fact tables that contain aggregated facts are oftencalled summary tables. A fact table usually contains facts with the same level of aggregation. Though most facts are additive, they can also be semi-additive or non-additive. Additive facts can be aggregated by simple arithmetical addition. A common example of this is sales. Non-additive facts cannot be added at all. An example of this is averages. Semi-additive facts can be aggregated along some of the dimensions and not along others. An example of this is inventory levels, where you cannot tell what a level means simply by looking at it.The image above illustrates a common example of asalesfact table and dimension tablescustomers,products,promotions,times, andchannels.
Snowflake schemas normalize dimensions to eliminate redundancy.The image abovepresents a graphical representation of a fact table within snowflake schema.
