In Dimensional modeling fact table is normalized or denormalized?in case of star

In Dimensional modeling, Star Schema: A Single Fact table will be surrounded by a group of Dimensional tables comprise of de- normalizeddat a Snowflake Schema: A Single Fact table will be surrounded by a group of Dimensional tables comprised of normalized dataThe Star Schema (sometimes referenced as star join schema) is the simplest data warehouse schema, consisting of a single "fact table" with a compound primary key, with one segment for each "dimension" and with additional columns of additive, numeric facts.The Star Schema makes multi-dimensionaldatabase
(MDDB) functionality possible using a traditional relational database. Because relational databases are the most common data management system in organizations today, implementing multi-dimensional views of data using a relational database is very appealing. Even if you are using a specific MDDB solution, its sources likely are relational databases. Another reason for using star schema is its ease of understanding. Fact tables in star schema are mostly in third normal form (3NF), but dimensional tables in de-normalized second normal form (2NF). If you want to normalize dimensional tables, they look like snowflakes (see snowflake schema) and the same problems of relational databases arise - you need complex queries and business users cannot easily understand the meaning of data. Although query
performance may be improved by advanced DBMS technology and hardware, highly normalized tables  make reporting difficult and applications complex.The Snowflake Schema is a more complex data warehouse model than a star schema, and is a type of star schema. It is called a snowflake schema because the diagram of the schema resembles a snowflake.Snowflake schemas normalize dimensions to eliminate redundancy. That is, the dimension data has been grouped into multiple tables instead of one
large table. For example, a product dimension table in a star schema might be normalized into a products table, a Product-category table, and a product-manufacturer table in a snowflake schema. While this saves space, it increases the number of dimension tables and requires more foreign key joins. The result is more complex queries and reduced query performance.