This continues and finishes my two-part series on warehouse load patterns. There are many methods to transfer rows between systems from a basic design perspective. This isn’t specific to any ETL tool but rather the basic patterns for moving data. The most difficult part in designing a pattern is efficiency. It has to be accurate and not adversely impact the source system, but this is all intertwined and dependent on efficiency. You only want to move the rows that have changed or been added since the previous ETL execution, deltas. This reduces the network load, the source system load (I/O, CPU, locking, etc.), the destination system load. Being efficient also improves the speed and as a direct result it increases the potential frequency for each ETL run, which has a direct impact on business value.
The pattern you select depends on many things. The previous part of the series covers generic design patterns and considerations for warehouse loads that can be applied to most of the ETL designs presented below. This section covers patterns I have used in various projects. I’m sure there are some patterns I have missed, but these cover the most used types that I have seen. These are not specific to any data engine or ETL tool, but the examples use SQL Server as a base for functionality considerations. Design considerations, columns available, administrative support, DevOps practices, reliability of systems, and cleanliness of data all come into consideration when choosing your actual ETL pattern.
Click through for a compendium of common patterns you can use to indicate that a row should go into a warehouse.