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Building ETL Processes for Business Intelligence Solutions Built on Microsoft SQL Server
October 2008
(Based on 2 reviews)
If you work with a business intelligence solution, then chances are pretty good that you need to pull data into a data warehouse or data mart. On the surface, this seems like it would be an easy task. All you have to do is gather data from various systems and load it into the data warehouse. Maybe you have to map a few columns because things are stored in different places, but all data is simple to interpret because it’s all stored in a nice relational database.Wouldn’t it be nice if that was the case? In the real world, data is stored in many different places in your organization. Some of it may be in relational databases, but some may be in flat files with no referential integrity rules. Pulling data from these sources and getting it into the common format of your data warehouse can be a monstrous task, one that takes you weeks to develop.
When it comes to designing a data warehouse, there are quite a few traditional data modeling processes that are useful.When you design a data model, you will typically gather requirements, identify entities and attributes based on the data, normalize the model, determine relationships, and document the model using a modeling tool such as CA ERwin Data Modeler. But now you have to work at getting data into the physical database described by the model.
In general terms, this process of gathering data from several disparate data sources, changing it to a common format, and loading it into a data warehouse, has come to be called Extract, Transform, and Load (ETL). There are many tools available that help you to build ETL processes, but you still need to have a firm understanding of the data in order to be successful.
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