WHAT’S IN THIS CHAPTER?
- The Lookup Transformation
- Using the Merge Join Transformation
- Building a basic package
- Using the Lookup Transformation
- Loading Lookup cache with the Cache Connection Manager and Cache Transform
In the simplest ETL scenarios, you use an SSIS Data Flow to extract data from a single source table and populate the corresponding destination table. In practice, though, you usually won’t see such trivial scenarios: the more common ETL scenarios will require you to access two or more data sources simultaneously and merge their results together into a single destination structure. For instance, you may have a normalized source system that uses three or more tables to represent the product catalog, whereas the destination represents the same information using a single denormalized table (perhaps as part of a data warehouse schema). In this case you would need to join the multiple source tables together in order to present a unified structure to the destination table. This joining may take place in the source query in the SSIS package or when using a Lookup Transform in an SSIS Data Flow.
Another less obvious example of joining data is loading a dimension that would need to have new rows inserted and existing rows updated in a data warehouse. The source data is coming from an OLTP database and needs to be compared to the existing dimension to find the rows that need updating. Using the dimension as a second source, you can then join the data ...