In this case, the star schema, although further denormalized, would only reduce the number or records by a (negligible) ~0.02% (=[1,000,000+300] instead of [1,000,000+300+220]) Some database developers compromise by creating an underlying snowflake schema with views built on top of it that perform many of the necessary joins to simulate a star schema. This provides the storage benefits achieved through the normalization of dimensions with the ease of querying that the star schema provides. The tradeoff is that requiring the server to perform the underlying joins automatically can result in a performance hit when querying as well as extra joins to tables that may not be necessary to fulfill certain queries. Benefits The snowflake schema is in the same family as the star schema logical model. In fact, the star schema is considered a special case of the snowflake schema. The snowflake schema provides some advantages over the star schema in certain situations, including: Some OLAP multidimensional database modeling tools are optimized for snowflake schemas. Normalizing attributes results in storage savings, the tradeoff being additional complexity in source query joins. Disadvantages The primary disadvantage of the snowflake schema is that the additional levels of attribute normalization adds complexity to source query joins, when compared to the star schema. Snowflake schemas, in contrast to flat single table dimensions, have been heavily criticised. Their goal is assumed to be an efficient and compact storage of normalised data but this is at the significant cost of poor performance when browsing the joins required in this dimension. This disadvantage may have reduced in the years since it was first recognized, owing to better query performance within the browsing tools. When compared to a highly normalized transactional schema, the snowflake schema's denormalization removes the data integrity assurances provided by normalized schemas. Data loads into the snowflake schema must be highly controlled and managed to avoid update and insert anomalies. Examples The example schema shown to the right is a snowflaked version of the star schema example provided in the star schema article. The following example query is the snowflake schema equivalent of the star schema example code which returns the total number of television units sold by brand and by country for 1997. Notice that the snowflake schema query requires many more joins than the star schema version in order to fulfill even a simple query. The benefit of using the snowflake schema in this