A data warehouse can have a star schema architecture or a snow flake architecture. You have to choose the one tht best fits your data, after reading their characteristics.
The star schema architecture is the simplest data warehouse schema. It is called a star schema because the diagram resembles a star, with points radiating from a center. The center of the star consists of fact table and the points of the star are the dimension tables. Usually the fact tables in a star schema are in third normal form(3NF) whereas dimensional tables are de-normalized. Despite the fact that the star schema is the simplest architecture, it is most commonly used nowadays.
The snowflake schema architecture is a more complex variation of the star schema used in a data warehouse, because the tables which describe the dimensions are normalized, so they have a typical relational database design. Snowflake schemas are generally used when a dimensional table becomes very big and when a star schema can’t represent the complexity of a data structure. For example if a PRODUCT dimension table contains millions of rows, the use of snowflake schemas should significantly improve performance by moving out some data to other table (with BRANDS for instance). The problem is that the more normalized the dimension table is, the more complicated SQL joins must be issued to query them. This is because in order for a query to be answered, many tables need to be joined and aggregates generated.