dimension table

A dimension table is a table in a star schema of a data warehouse. A dimension table stores attributes, or dimensions, that describe the objects in a fact table.

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A dimension table is a table in a star schema of a data warehouse. A dimension table stores attributes, or dimensions, that describe the objects in a fact table.

In data warehousing, a dimension is a collection of reference information about a measurable event. These events are known as facts and are stored in a fact table. Dimensions categorize and describe data warehouse facts and measures in ways that support meaningful answers to business questions.  They form the very core of dimensional modeling. 

A data warehouse organizes descriptive attributes as columns in dimension tables.  For example, a customer dimension’s attributes could include first and last name, birth date, gender, etc., or a website dimension would include site name and URL attributes. 

A dimension table has a primary key column that uniquely identifies each dimension record (row).  The dimension table is associated with a fact table using this key.  Data in the fact table can be filtered and grouped (“sliced and diced”) by various combinations of attributes.  For example, a Login fact with Customer, Website, and Date dimensions can be queried for “number of males age 19-25 who logged in to funsportsite.com more than once during the last week of September 2010, grouped by day.”

Dimension tables are referenced by fact tables using keys. When creating a dimension table in a data warehouse, a system-generated key is used to uniquely identify a row in the dimension. This key is also known as a surrogate key. The surrogate key is used as the primary key in the dimension table. The surrogate key is placed in the fact table and a foreign key is defined between the two tables. When the data is joined, it does so just as any other join within the database.

Like fact tables, dimension tables are often highly de-normalized, because these structures are not built to manage transactions they are built to enable users to analyze data as easily as possible.

This was first published in April 2012

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