This document discusses different types of dimension tables commonly used in data warehouses. It describes slowly changing dimensions, rapidly changing dimensions, junk dimensions, inferred dimensions, conformed dimensions, degenerate dimensions, role playing dimensions, shrunken dimensions, and static dimensions. Dimension tables contain attributes and keys that provide context about measures in fact tables.

1. Types of Dimension Tables in a Data
Warehouse
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1
Ch Anwar ul Hassan (Lecturer)
Department of Computer Science and Software Engineering
Capital University of Sciences & Technology, Islamabad
Pakistan
anwarchaudary@gmail.com
Intro to Data Warehousing

2.  A dimension is something that qualifies the quantity
or measures.
 Dimension table contains the data about the
business. The primary keys of the dimension tables
are used in Fact tables with Foreign key
relationship. And the remaining columns in the
dimension is normal data which is the information
about the Objects related to the business.
What is Dimension?

3.  A Dimension table is a table in a star schema of a
data warehouse.
 Data warehouses are built using dimensional data
models which consist of fact and dimension tables.
 Dimension tables are used to describe dimensions. They
contain dimension keys, values and attributes.
Dimension Table

4. Commonly used dimension tables in data
warehouse
 Slowly Changing Dimensions
 Rapidly Changing Dimensions
 Junk Dimensions
 Inferred Dimensions
 Conformed Dimensions
 Degenerate Dimensions
 Role Playing Dimensions
 Shrunken Dimensions
 Static Dimensions
Types of Dimensions

5.  Dimensions that change very slowly overtime
rather than according to regular schedule.
Attributes like name, address can change but not too
often.
 Below are some popular approaches of
SCDs:
 Type 0: This is Passive method. The records
which were inserted will not change any time.
Slowly Changing Dimensions

6.  Type 1: This method overwrites the old record with
new values without tracking any historical data.
 The advantage of type 1 is that it is very easy to follow and it
results in huge space savings and hence cost savings.
 The disadvantage is that no history is maintained.
Slowly Changing Dimensions

7.  Type 2: This methods tracks the historical data by
using version number or by using startdate,
enddate columns. The drawback is the table will
grow vertically. And it requires more space.
Slowly Changing Dimensions

8.  Type 3: This method will also track the historical
data by inserting the column with new value. It
preserves limited history.
 Type 4: This method uses a separate table for
storing all the historical data and main table will
hold the current_data. And both will pointed with
the same surrogate key.
Slowly Changing Dimensions

9.  Type 3: This method will also track the historical
data by inserting the column with new value. It
preserves limited history.
 Type 4: This method uses a separate table for
storing all the historical data and main table will
hold the current_data. And both will pointed with
the same surrogate key.
Slowly Changing Dimensions

10.  The Dimension which contains rapidly changing attributes.
If we maintain any historical data for these type of tables.
We will definitely get an issue related to memory and
performance.
 The solution is to maintain mini dimension tables for
historical data like type 4 Dimension in SCD. The main
table should contain the current values and mini
dimensions can contains historical data.
Rapidly Changing Dimensions

11. Junk Dimension
A junk dimension is a single table with a combination of
different and unrelated attributes to avoid having a
large number of foreign keys in the fact table. They are
often created to manage the foreign keys created by
rapidly changing dimensions.
As per above, if we consider
Gender_Marital_Status we
can use only 1 single column
in Fact table.

12. Inferred Dimensions: The Dimension which is important to
create a fact table but it is not yet ready, then we can
assign some dummy details for one ID and we can use that
ID in fact table. After getting the details then we can update
the details in the dimension.
Inferred Dimensions

13.  Conformed Dimensions: Dimensions which are
connected with multiple fact tables are know as
conformed Dimension.
 Eg: Customer Dimension and Product Dimension are
required in Shipment Fact, Sales fact and Service
Request Fact
Conformed Dimension

14.  Conformed Dimensions: Dimensions which are
connected with multiple fact tables are know as
conformed Dimension.
 Eg: Product, Date and Store Dimension Table are
connected with both Sales and Inventory fact tables.
Conformed Dimension

15. Degenerate Dimensions: A degenerate table does not
have its own dimension table. It is derived from a fact table.
The column (dimension) which is a part of fact table but
does not map to any dimension.
Eg: SalesOrderNumber in Fact table
Degenerate Dimension

16.  Role Playing Dimensions: The same dimension
which can be used for multiple purpose
 Eg: Date Dimension can be used as Order day, Ship
day, Close Day etc.
Role play dimension

17. Shrunken Dimensions: A shrunken dimension is a subset
of another dimension.
Shrunken Dimensions

18. Static dimensions are not extracted from the
original data source, but are created within the
context of the data warehouse. A static dimension
can be loaded manually — for example with status
codes — or it can be generated by a procedure,
such as a date or time dimension.
Static Dimensions