Kimball University: Six Key Decisions for ETL Architectures
Best-practice advice on software vs. coding, where to integrate, how to capture changed data, when to stage data, where to correct data and what latency levels to shoot for.
3. Which change data capture mechanisms should we choose?
During the data warehouse's initial historic load, capturing source data content changes is not important since you are loading all data from a point in time forward. However, most data warehouse tables are so large that they cannot be refreshed during every ETL cycle. You must have a capability to transfer only the relevant changes to the source data since the last update. Isolating the latest source data is called change data capture (CDC). The idea behind change data capture is simple enough: just transfer the data that has been changed since the last load.
But building a good change data capture system is not as easy as it sounds. Realize that the mechanism selected must be absolutely fool proof -- all changed data must be identified. Finding the most comprehensive strategy can be elusive; many times updates to source system tables can occur outside the application itself. A mistake here will result in inconsistent results that can't be easily explained; often it takes significant data reconciliation to identify the culprit. Problems can be a very costly in terms of rework -- not to mention embarrassing. In short, capturing data changes is far from a trivial task, and you must clearly understand the source data systems. This knowledge will help the ETL team evaluate data sources, identify change data capture problems and determine the most appropriate strategy.
4. When should we stage the data?
In today's data warehousing environment, it's quite possible for ETL tools to establish a direct connection to the source database, extract and stream the data through the ETL tool to apply any required transformation in memory, and finally write it, only once, into the target data warehouse table. From a performance viewpoint, this is a great capability as writes, especially logged writes into the RDBMS, are very expensive; it's a good design goal to minimize them. However, despite the performance advantages, this may not be the best approach. There are several reasons an organization might decide to physically stage the data (i.e., write it to disk) during the ETL process:
The most appropriate CDC method requires a compare of the current copy of the source table to the prior copy of the same table.
The organization has elected to stage the data immediately after extract for archival purposes -- possibly to meet compliance and audit requirements.
A recovery/restart point is desired in the event the ETL job fails in midstream -- potentially due to a break in the connection between the source and ETL environment.
Long running ETL processes may open a connection to the source system that create problems with database locks and that stresses the transaction system.
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