Reading Notes: The Data Warehouse Toolkit 2nd

The key architectural requirement for the data staging area is that it is off-limits to business users and does not provide query and presentation services.

It is acceptable to create a normalized database to support the staging processes; however, this is not the end goal. The normalized structures must be off-limits to user queries because they defeat understandability and performance. As soon as a database supports query and presentation services, it must be considered part of the data warehouse presentation area. By default, normalized databases are excluded from the presentation area, which should be strictly dimensionally structured.

A row in a fact table corresponds to a measurement. A measurement is a row in a fact table. All the measurements in a fact table must be at the same grain.

The most useful facts in a fact table are numeric and additive.

Dimension tables are the entry points into the fact table. Robust dimension attributes deliver robust analytic slicing and dicing capabilities. The dimensions implement the user interface to the data warehouse.

A careful grain statement determines the primary dimensionality of the fact table. It is then often possible to add more dimensions to the basic grain of the fact table, where these additional dimensions naturally take on only one value under each combination of the primary dimensions. If the additional dimension violates the grain by causing additional fact rows to be generated, then the grain statement must be revised to accommodate this dimension.

You must avoid null keys in the fact table. A proper design includes a row in the corresponding dimension table to identify that the dimension is not applicable to the measurement.