The table structure depicted in Figure 16-5 is called the star schema. It is based on the many-to-one relationships between the facts and their dimensions: each dimension record is used by many facts; a fact’s foreign key points to a single dimension record.

Figure 16-5. The many-to-one relationship between facts and their dimensions

Another predominant analytical model is the snowflake schema. The snowflake schema is based on the same building blocks: facts and dimensions. However, in the snowflake schema, the dimensions are multilevel: each dimension is further normal‐ ized into more fine-grained dimensions, as shown in Figure 16-6.

As a result of the additional normalization, the snowflake schema will use less space to store the dimension data and is easier to maintain. However, querying the facts’ data will require joining more tables, and therefore, more computational resources are needed.

Both the star and snowflake schemas allow data analysts to analyze business perfor‐ mance, gaining insights into what can be optimized and built into business intelli‐ gence (BI) reports.

Figure 16-6. Multilevel dimensions in the snowflake schema