Definition: As per Kimball, when in dimension in the DWH is linked with another dimension table, the secondary table is call outrigger dimension. For example a customer dimension referring to the Date dimension for the date of account creation.
Now, it might seem like snowflaked(not complete), but it is not. There are a few differences as detailed below:
Now, it might seem like snowflaked(not complete), but it is not. There are a few differences as detailed below:
- Snowflake make the structure highly normalized where as outrigger makes it partially normalized.
- Snowflake makes the ETL complicated and reduces the query performance.
While, Now outrigger dimensions are similar to snowflake but are vary limited in nature. They are just one or more levels removed fro the fact table. This is mostly used when there is a business requirement to slice and dice of the particular column.
Consider the following criteria before creating an outrigger dimension:
- User Analytics Needed: If the user wants to slice and dice on the column
- Data Granularity vs Cardinality : A column with high low granularity and high cardinality is a candidate.
- Update Frequency : If a column is updated very frequently, not more than once a month
- It is divided in the source itself.
- Customer and it location would be a good candidate for outrigger dimension
For list of dimensions in data-warehouse, click here
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