If you’re into business intelligence, data warehousing and analytics, you will have heard an endless number of references to Bill Inmon and Ralph Kimball. These two figureheads in datawarehousing architecture have produced an immense number of books, articles, training seminars, etc. While many of their strategies and modelling approaches are similar, they have near-opposite views on other aspects.
We’ve talked a lot about optimizing queries and query performance, but we haven’t really touched that much on the storage and data modelling aspects. In this series of post, I’ll run through some basic tips on how you can more efficiently model and store your data, which may come in particularly handy when you’re working with large databases and large transaction volumes, but a lot of it also makes good design sense in smaller databases.
In this first article, we’ll cover the normalized data model.