Dimensional data model is most often used in data warehousing systems. This is different from the 3rd normal form, commonly used for transactional (OLTP) type systems. As you can imagine, the same data would then be stored differently in a dimensional model than in a 3rd normal form model.
To understand dimensional data modeling, let's define some of the terms commonly used in this type of modeling:
Dimension: A category of information. For example, the time dimension.
Attribute: A unique level within a dimension. For example, Month is an attribute in the Time Dimension.
Hierarchy: The specification of levels that represents relationship between different attributes within a dimension. For example, one possible hierarchy in the Time dimension is Year → Quarter → Month → Day.
To understand dimensional data modeling, let's define some of the terms commonly used in this type of modeling:
Dimension: A category of information. For example, the time dimension.
Attribute: A unique level within a dimension. For example, Month is an attribute in the Time Dimension.
Hierarchy: The specification of levels that represents relationship between different attributes within a dimension. For example, one possible hierarchy in the Time dimension is Year → Quarter → Month → Day.
In the star schema design, a single object (the fact table) sits in the middle and is radially connected to other surrounding objects (dimension lookup tables) like a star. Each dimension is represented as a single table. The primary key in each dimension table is related to a forieng key in the fact table.
Sample star schema
Sample star schema
All measures in the fact table are related to all the dimensions that fact table is related to. In other words, they all have the same level of granularity.
A star schema can be simple or complex. A simple star consists of one fact table; a complex star can have more than one fact table.
A star schema can be simple or complex. A simple star consists of one fact table; a complex star can have more than one fact table.
Conceptual, Logical, And Physical Data Models
The three level of data modeling, conceptual data model, logical data model, and physical data model, were discussed in prior sections. Here we compare these three types of data models.
Below we show the conceptual, logical, and physical versions of a single data model.
Below we show the conceptual, logical, and physical versions of a single data model.
Conceptual Model Design
Logical Model Design
Physical Model Design
Reference website - http://www.1keydata.com/datawarehousing/
Date - 23-Feb-2010
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