Introduction of Joining Data in SSIS
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What’s In ThisTopic?
- 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 CacheTransform
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 using a Merge Join Transformation in your Data Flow. The joined rows can then be compared to look for changes. This type of loading is discussed in Loading a Data Warehouse Topic. In the relational world, such requirements can be met by employing a relational join operation if the data exists in an environment where these joins are possible. When you are creating ETL projects, the data is often not in the same physical database, the same brand of database, the same server, or, in the worst cases, even the same physical location (all of which typically render the relational join method useless). In fact, in one common scenario, data from a mainframe system needs to be joined with data from SQL Server to create a complete data warehouse in order to provide your users with one trusted point for reporting. The ETL solutions you build need to be able to join data in a similar way to relational systems, but they should not be constrained to having the source data in the same physical database. SQL Server Integration Services (SSIS) provides several methods for performing such joins, ranging from functionality implemented in Data Flow Transformations to custom methods implemented in T-SQL or managed code.
This Topic explores the various options for performing joins and provides guidelines to help you determine which method you should use for various circumstances and when to use it. After reading this Topic, you should be able to optimize the various join operations in your ETL solution and understand their various design, performance, and resource trade-offs.