A variant of the Star Schema where dimension tables are normalized into multiple related tables — trades query simplicity and JOIN performance for reduced storage and easier dimension hierarchy updates.
Snowflake Schema looks like a Star Schema with the dimensions further split: product → product_subcategory → product_category, instead of all three columns in a flat dim_product. Storage is more efficient (less redundancy) and hierarchy changes are easier (update one row in product_category instead of every product). The cost: queries require more JOINs, and the model is harder for analysts to navigate. Modern columnar warehouses (Snowflake, BigQuery) mostly eliminate the storage benefit, so flat Star Schemas usually win.
Choosing Star Schema over Snowflake Schema for a new Snowflake data warehouse — columnar compression makes the denormalization 'cost' trivial.
Snowflake Schema vs Star Schema is the classic warehouse-modeling debate — in columnar warehouses, Star almost always wins; in row-oriented systems, Snowflake's storage savings still matter.
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