Here's an example of a design decision process that would lead you to a Snowflake dimension. Start by assuming that all the dimensions in the Data Mart (versus the Data Warehouse, where we may have different ideas) will be modeled as Stars.

We start in our first design with a single dimension, Geography, containing the following columns:

We have one fact source table containing, say, population data with the following columns:

In ETL, we would join this source table to the dimension table on the business key PostalCode to retrieve the surrogate key and use this to load the data mart fact table:

Now, let's introduce a second fact source table containing projected population data, but with a different grain. Let's assume this data comes in, not at the Postal Code grain but rather at the State grain. We'd have a source table with columns such as follows:

We can't join this new source table to our existing Geography dimension because if we do so, we will get back many surrogate keys—each representing one postal code within the specified state. So, we need to Snowflake (partially normalize) the Geography dimension so that it will support the grain of each of our fact source tables, giving us two dimension tables similar to the the following two bullet lists:

dimGeography:

and dimGeographyState:

Notice that we did not fully normalize the dimension (postal code and city both exist in the first table, state and country in the second). We just normalized the dimension enough to give us a single relationship between each of our two facts and this dimension.