This is a more normalized structure and leads to more difficult queries and slower response times. Figure 4 represents the beginning of the snowflake process. The Category hierarchy is being broken out of the ProductDimension table. Since the purpose of our OLAP system is to speed queries, snowflaking usually isn't something you want to do. Realize that if you build cubes, you don't have to worry about the query slowdown, although it might take a little longer to build the cube.
Some people try to normalize the dimension tables to save space.
However, in the overall scheme of the data warehouse, the dimension tables usually only hold about 1 percent of the total records. Therefore, any space savings from normalizing, or snowflaking, are negligible. Fact table size You've already seen how 500 products sold in 200 stores and tracked for 10 years could produce 365,000,000 records in a fact table with a daily grain. This, however, is the maximum size for the table. Most of the time, you don't have this many records in the fact table.