Tables and schemas types

Fact tables contain foreign keys and data that can be summarized. For example, the fact inventory table contains foreign keys to the date, location, and product dimension tables. The summarizable data item is the quantity in stock. The primary key of the table is the concatenation of two or more of the foreign keys (all of which are arbitrary integer keys). Data warehouse uses referential integrity to ensure that the arbitrary foreign keys, used in the fact tables, reference existing dimension table rows. However, unlike true relational databases, the dimensional model foreign keys are always meaningless. Data warehouses also enforce nonnull, unique primary keys.Dimension tables contain descriptive data. The dimension_date table, for example, has its unique integer key, along with attributes for the many ways in which a person might want to use a date in a query. There will be one row in a date dimension table for each date that might be used by any query against the data warehouse. Dimension tables spell out the analysis that the users want to perform. Fact tables, on the other hand, contain numerical measures the users want to analyze. Snowflake schemaThe snowflake schema aims to normalize the star schema's denormalized data. When the star schema's dimensions are intricate, highly structured, and have numerous degrees of connection, and the kid tables have several parent tables, the snowflake structure emerges. Some of the star schema's common issues are resolved by the snowflake schemaStar schemaThe star schema is the most straightforward method for arranging data in the data warehouse. Any or even more Fact Tables that index a number of Dimension Tables may be present in the star schema's central area. Dimensions Keys, Values, and Attributes are found in Dimension Tables, which are used to define Dimensions. The star schema's objective is to distinguish between the descriptive or Dimensional data and the numerical "FACT" data that pertains to a business.