Understanding the Star schema:

The most common modeling paradigm in the star schema, in which the data warehouse contains (1) a large central table (fact table) containing the bulk of the data, with no redundancy, and (2) a set of smaller attendant tables (dimension tables), one for each dimension. The schema graph resembles a starburst, with the dimension tables displayed in a radial pattern around the central fact table.

An example of Star schema for AllElectronics sales is shown:

Note: - Sales are considered along four dimensions, namely, time, branch, location. The schema contains a central fact table for sales that contains keys to each of the four dimensions, along with two measures: Dollars_sold and units_sold. To minimize the size of the fact table, dimension identifiers (such as time_key and item_key) are system generated identifiers. The Star schema, each dimension is represented by only one table, and each table contains a set of attributes. For example, the location dimension table contains the attributes set{location_key, Street, city,province_or_state, country}. This constraint may introduce some redundancy for example, “Vancouver” and “Victoria” are both cities in the location dimension table will create redundancy among the attributes province_or_state_and country that is, (…..., Vancouver, British Columbia, Canada ) and  (….. , Vancouver, British Columbia, Canada ) . moreover, the attributes within a dimension table may form either a hierarchy(total order) or a lattice (partial order).