SQL Server introduced in SQL Server 2008. It is very useful statement specially for data warehousing environment. So, it is very likely that you will need it in SSIS project. The can be two solution to incorporate in SSIS.
First One:
Using the merge join operator, do the followings:
![](data:image/png;base64,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)
Second One:
Using the "Slowly Changing Dimension" operator, do the followings -
![](data:image/png;base64,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)
If you compare these two figures, you will find that how easy it is. The SCD really does a lot for you. It encapsulates sort, merge join and conditional split and auto generate the OLE DB command. So, no programmer forgets to use it instead of using the first option or calling script task from SSIS and make the code less programmer friendly.
If the table size is large, then the first option should be used. On the other hand, if the table size is small, performance of first and second option is almost same. For detail, please read this article -
http://chrisjarrintaylor.co.uk/
First One:
Using the merge join operator, do the followings:
- Merge Join - Source Left join Destination
- Conditional Split -
- Destination ID is null - "OLEDB Destination"
- Destination ID is not null - "OLEDB Command". Use update operation within "OLEDB Command"
Second One:
Using the "Slowly Changing Dimension" operator, do the followings -
- Add the "Slowly Changing Dimension" and configure it using wizard
- Using OLEDB Connection, choose the destination table and set its ID as "Business key"
- After clicking next, select all other destination columns and select the Change Type as "Changing attribute"
- Keep all others default.
If you compare these two figures, you will find that how easy it is. The SCD really does a lot for you. It encapsulates sort, merge join and conditional split and auto generate the OLE DB command. So, no programmer forgets to use it instead of using the first option or calling script task from SSIS and make the code less programmer friendly.
If the table size is large, then the first option should be used. On the other hand, if the table size is small, performance of first and second option is almost same. For detail, please read this article -
http://chrisjarrintaylor.co.uk/