Slowly Changing Dimension in SSIS

The "Slowly Changing Dimension" problem is a common one particular to data warehousing. This applies to cases where the attribute for a record varies over time. 

We give an example below:

Babu is a customer with XYZ Inc. He first lived in Chicago, Illinois. So, the original entry in the customer 

lookup table has the following record:

Customer Key	Name	State
1001	Babu	Illinois

At a later date, He moved to Los Angeles, California on January, 2003. How should XYZ Inc. now modify its customer table to reflect this change? This is the "Slowly Changing Dimension" problem.

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. Therefore, the customer is treated essentially as two people.

Type 3: The original record is modified to reflect the change.

We next take a look at each of the scenarios and how the data model and the data looks like for each of them. Finally, we compare and contrast among the three alternatives.

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	Babu	Illinois

After Babu moved from Illinois to California, the new information replaces the new record, and we have the following table:

Customer Key	Name	State
1001	Babu	California

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 Christina lived in Illinois before.

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.

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	Babu	Illinois

After Babu moved from Illinois to California, we add the new information as a new row into the table:

Customer Key	Name	State
1001	Babu	Illinois
1005	Babu	California

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.

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.

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	Babu	Illinois

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

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

After Babu moved from Illinois to California, the original information gets updated, and we have the following table (assuming the effective date of change is January 15, 2003):

Customer Key	Name	Original State	Current State	Effective Date
1001	Babu	Illinois	California	15-JAN-2003

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 Christina later moves to Texas on December 15, 2003, the California information will be lost.

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.

Different Date Formats with Convertion

SELECT CONVERT(VARCHAR(20),GETDATE(),100) -- Jul  2 2012  6:34PM

SELECT CONVERT(VARCHAR(20),GETDATE(),101) -- 07/02/2012

SELECT CONVERT(VARCHAR(20),GETDATE(),102) -- 2012.07.02

SELECT CONVERT(VARCHAR(20),GETDATE(),103) -- 02/07/2012

SELECT CONVERT(VARCHAR(20),GETDATE(),104) -- 02.07.2012

SELECT CONVERT(VARCHAR(20),GETDATE(),105) -- 02-07-2012

SELECT CONVERT(VARCHAR(20),GETDATE(),106) -- 02 Jul 2012

SELECT CONVERT(VARCHAR(20),GETDATE(),107) -- Jul 02, 2012

SELECT CONVERT(VARCHAR(20),GETDATE(),108) -- 18:34:54

SELECT CONVERT(VARCHAR(20),GETDATE(),109) -- Jul  2 2012  6:34:54

SELECT CONVERT(VARCHAR(20),GETDATE(),110) -- 07-02-2012

SELECT CONVERT(VARCHAR(20),GETDATE(),111) -- 2012/07/02

SELECT CONVERT(VARCHAR(20),GETDATE(),112) -- 20120702

SELECT CONVERT(VARCHAR(20),GETDATE(),113) -- 02 Jul 2012 18:34:54

SELECT CONVERT(VARCHAR(20),GETDATE(),114) -- 18:34:54:707

SELECT CONVERT(VARCHAR(20),GETDATE(),120) -- 2012-07-10 06:45:14

SELECT CONVERT(VARCHAR(20),GETDATE(),121) -- 2012-07-10 06:45:14.

SELECT CONVERT(VARCHAR(20),GETDATE(),126) -- 2012-07-10T06:45:14.

SELECT CONVERT(VARCHAR(20),GETDATE(),127) -- 2012-07-10T06:45:14.

SELECT CONVERT(VARCHAR(20),GETDATE(),130) -- 21 ????? 1433  6:45:

SELECT CONVERT(VARCHAR(20),GETDATE(),131) -- 21/08/1433  6:45:14: