To load data from OLTP system to DW, we have to face a problem: how to balance time and cost. Since data raises faster and faster, we need to increase our hardware ability to match the time requirement. So, incremental load coming out to reduce the data transmission significantly. There are three way to achieve it: 1. use datetime column 2. Changed data capture 3. changed data tracking. For a long time, I use the first way to capture the changed data manual, it is good, but too many works in development and testing. Here I want to introduce the CDC. Basically, CDC just a feature to utilize LSN(Log Sequence Number) and log tables to capture changed data, while SSIS itself provide native components to easy work with this new feature.

Let’s build a CDC workflow in SSIS for example:

Enable CDC feature

• enable CDC by executing sp_cdc_enable_db(disable by sp_cdc_disable_db). Then check it with select name from sys.databaseswhere is_cdc_enabled=1
• enable CDC for spec table with sp_cdc_enable_table then we can find the CDC table in systemtable folder or using select namefrom sys.tables tabwhere is_tracked_by_cdc=1 to check.

exec sys.sp_cdc_enable_table
@source_schema = N'Person'
, @source_name = N'Address'
, @role_name = N'cdc_Admin'
, @capture_column_list = N'column1, column2'; //can track spec columns, rather than the whole table

Control flow setting

• add CDC control task
• set CDC control operation to mark cdc start and set the cdc states for saving cdc states
• run this control task, it will create a record in tablecdc_states
• create two CDC control tasks , one set operation to Get Processing Range , another for Mark process range, they will get changed data and update CDC states respectively.
• put a dataflow which is response for ETL operation, between two CDC control tasks.

Data flow setting into staging table

• add CDC source which points to the table enabled CDC and choose the correct cdc_states table as well.
• choose Net CDC processing mode in CDC source.
• add CDC splinter after CDC source, create three Derived Column transformation for insert(0), update(2) and delete(1) data.
• create a Union All transformation to union all data and export to stage database.
• if necessary, we need to add a truncate script before all control flow to delete everything in stage database.
  

Update fact table through staging tables

• create a oledb source to connect to stage database
• use conditional split to split insert and update+delete
• for insert, we directly export; for update+delete we need to delete from fact table by identifier by OLE DB Command transformation, and use conditional split to export update data.
• if necessary, use lookup to replace some dimensional columns
• export to fact database.

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