Dimensional Data Modeling
Dimensional Data Modeling comprises of one or more dimension tables and fact tables. Good examples of dimensions are location, product, time, promotion, organization etc. Dimension tables store records related to that particular dimension and no facts(measures) are stored in these tables.
For example, Product dimension table will store information about products(Product Category, Product Sub Category, Product and Product Features) and location dimension table will store information about location( country, state, county, city, zip. A fact(measure) table contains measures(sales gross value, total units sold) and dimension columns. These dimension columns are actually foreign keys from the respective dimension tables.
Example of Dimensional Data Model: Figure 1.6
In the example figure 1.6, sales fact table is connected to dimensions location, product, time and organization. It shows that data can be sliced across all dimensions and again it is possible for the data to be aggregated across multiple dimensions. "Sales Dollar" in sales fact table can be calculated across all dimensions independently or in a combined manner which is explained below.
- Sales Dollar value for a particular product
- Sales Dollar value for a product in a location
- Sales Dollar value for a product in a year within a location
- Sales Dollar value for a product in a year within a location sold or serviced by an employee
In Dimensional data modeling, hierarchies for the dimensions are stored in the dimensional table itself. For example, the location dimension will have all of its hierarchies from country, state, county to city. There is no need for the individual hierarchial lookup like country lookup, state lookup, county lookup and city lookup to be shown in the model.
Uses of Dimensional Data Modeling
Dimensional Data Modeling is used for calculating summarized data. For example,
sales data could be collected on a daily basis and then be aggregated
to the week level, the week data could be aggregated to the month level,
and so on. The data can then be referred to as aggregate data. Aggregation
is synonymous with summarization, and aggregate data is synonymous with
summary data. The performance of dimensional data modeling can be significantly increased when materialized views are used. Materialized view is a pre-computed table comprising aggregated or joined data from fact and possibly dimension tables which also known as a summary or aggregate table.
Dimension Table
Dimension table is one that describe the business entities of an enterprise,
represented as hierarchical, categorical information such as time, departments,
locations, and products. Dimension tables are sometimes called lookup
or reference tables.
Location Dimension
In a relational data modeling, for normalization purposes, country lookup, state lookup, county lookup, and city lookups are not merged as a single table. In a dimensional data modeling(star schema), these tables would be merged as a single table called LOCATION DIMENSION for performance and slicing data requirements. This location dimension helps to compare the sales in one region with another region. We may see good sales profit in one region and loss in another region. If it is a loss, the reasons for that may be a new competitor in that area, or failure of our marketing strategy etc.
Example of Location Dimension: Figure 1.8
In the above example, the location part of the Dimensional data model diagram is shown for easy understanding. It shows all the lookups country, state, county and city are connected to the single location dimension. Below are the data stored in each table found in the above location part. Dimension tables have been explained in detail under the section Dimensions.
Country Lookup
| Country Code | Country Name | DateTimeStamp |
| USA | United States Of America | 1/1/2005 11:23:31 AM |
State Lookup
| State Code | State Name | DateTimeStamp |
| NY | New York | 1/1/2005 11:23:31 AM |
| FL | Florida | 1/1/2005 11:23:31 AM |
| CA | California | 1/1/2005 11:23:31 AM |
| NJ | New Jersey | 1/1/2005 11:23:31 AM |
County Lookup
| County Code | County Name | DateTimeStamp |
| NYSH | Shelby | 1/1/2005 11:23:31 AM |
| FLJE | Jefferson | 1/1/2005 11:23:31 AM |
| CAMO | Montgomery | 1/1/2005 11:23:31 AM |
| NJHU | Hudson | 1/1/2005 11:23:31 AM |
City Lookup
| City Code | City Name | DateTimeStamp |
| NYSHMA | Manhattan | 1/1/2005 11:23:31 AM |
| FLJEPC | Panama City | 1/1/2005 11:23:31 AM |
| CAMOSH | San Hose | 1/1/2005 11:23:31 AM |
| NJHUJC | Jersey City | 1/1/2005 11:23:31 AM |
Location Dimension
| Location Dimension Id | Country Name | State Name | County Name | City Name | DateTimeStamp |
| 1 | USA | New York | Shelby | Manhattan | 1/1/2005 11:23:31 AM |
| 2 | USA | Florida | Jefferson | Panama City | 1/1/2005 11:23:31 AM |
| 3 | USA | California | Montgomery | San Hose | 1/1/2005 11:23:31 AM |
| 4 | USA | New Jersey | Hudson | Jersey City | 1/1/2005 11:23:31 AM |
Relational vs Dimensional