SCD Type1,2 and 3

SCD Type 1, SCD Type 2, SCD Type 3,Slowly Changing Dimension Types,Advantages & Disadvantages

The Slowly Changing Dimension problem is a common one particular to data warehousing.There are in general three ways to solve this type of problem, and they are categorized as follows:

• Type 1: The new record replaces the original record. No trace of the old record exists.
• Type 2: A new record is added into the customer dimension table.Thereby, the customer is treated essentially as two people.
• Type 3: The original record is modified to reflect the change.

SCD Type 1,Slowly Changing Dimension Use,Example,Advantage,Disadvantage

In Type 1 Slowly Changing Dimension, the new information simply overwrites the original information. In other words, no history is kept.

In our example, recall we originally have the following table:

Customer Key	Name	State
1001	Williams	New York

After Williams moved from New York to Los Angeles, the new information replaces the new record, and we have the following table:

Customer Key	Name	State
1001	Williams	Los Angeles

Advantages

• This is the easiest way to handle the Slowly Changing Dimension problem, since there is no need to keep track of the old information.

Disadvantages

• All history is lost. By applying this methodology, it is not possible to trace back in history. For example, in this case, the company would not be able to know that Williams lived in New York before.

Usage

About 50% of the time.

When to use Type 1

Type 1 slowly changing dimension should be used when it is not necessary for the data warehouse to keep track of historical changes.

SCD Type 2,Slowly Changing Dimension Use,Example,Advantage,Disadvantage

In Type 2 Slowly Changing Dimension, a new record is added to the table to represent the new information. Therefore, both the original and the new record will be present. The new record gets its own primary key.

In our example, recall we originally have the following table:

Customer Key	Name	State
1001	Williams	New York

After Williams moved from New York to Los Angeles, we add the new information as a new row into the table:

Customer Key	Name	State
1001	Williams	New York
1005	Williams	Los Angeles

Advantages

• This allows us to accurately keep all historical information.

Disadvantages

• This will cause the size of the table to grow fast. In cases where the number of rows for the table is very high to start with, storage and performance can become a concern.
• This necessarily complicates the ETL process.

Usage

About 50% of the time.

When to use Type 2

Type 2 slowly changing dimension should be used when it is necessary for the data warehouse to track historical changes.

SCD Type 3,Slowly Changing Dimension Use,Example,Advantage,Disadvantage

In Type 3 Slowly Changing Dimension, there will be two columns to indicate the particular attribute of interest, one indicating the original value, and one indicating the current value. There will also be a column that indicates when the current value becomes active.

In our example, recall we originally have the following table:

Customer Key	Name	State
1001	Williams	New York

To accommodate Type 3 Slowly Changing Dimension, we will now have the following columns:

• Customer Key
• Name
• Original State
• Current State
• Effective Date

After Williams moved from New York to Los Angeles, the original information gets updated, and we have the following table (assuming the effective date of change is February 20, 2010):

Customer Key	Name	Original State	Current State	Effective Date
1001	Williams	New York	Los Angeles	20-FEB-2010

Advantages

• This does not increase the size of the table, since new information is updated.
• This allows us to keep some part of history.

Disadvantages

• Type 3 will not be able to keep all history where an attribute is changed more than once. For example, if Williams later moves to Texas on December 15, 2003, the Los Angeles information will be lost.

Usage

Type 3 is rarely used in actual practice.

When to use Type 3

Type III slowly changing dimension should only be used when it is necessary for the data warehouse to track historical changes, and when such changes will only occur for a finite number of time.