With good naming and datatyping conventions, an automated script can help you with the process of creating foreign key constraints across your database, or actually, suggest table relations where you’ve forgotten to implement them.
In datawarehousing, a common scenario you may have encountered is the phenomenon of late-arriving dimension members. Imagine a situation where you get dimension members and fact data from two entirely different data sources, which may or may not provide data at the same time. Some fact data will probably be ready to load before its corresponding dimension member(s) arrive. How do you tag your fact data, and what consequence does that have for the integrity of your data warehouse?
This week’s installment in the series on efficient data is all about data types. Be smart about choosing them; The right type for the right job will not only store your data in less physical space and enforce data integrity better, but smaller indexes and tables will also improve overall query performance.