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  2. Fact table - Wikipedia

    en.wikipedia.org/wiki/Fact_table

    Example of a star schema; the central table is the fact table. In data warehousing, a fact table consists of the measurements, metrics or facts of a business process. It is located at the center of a star schema or a snowflake schema surrounded by dimension tables. Where multiple fact tables are used, these are arranged as a fact constellation ...

  3. Degenerate dimension - Wikipedia

    en.wikipedia.org/wiki/Degenerate_dimension

    For example, the Oracle FAQ defines a degenerate dimension as a "data dimension that is stored in the fact table rather than a separate dimension table. This eliminates the need to join to a dimension table. You can use the data in the degenerate dimension to limit or 'slice and dice' your fact table measures." [3]

  4. Star schema - Wikipedia

    en.wikipedia.org/wiki/Star_schema

    Fact_Sales is the fact table and there are three dimension tables Dim_Date, Dim_Store and Dim_Product. Each dimension table has a primary key on its Id column, relating to one of the columns (viewed as rows in the example schema) of the Fact_Sales table's three-column (compound) primary key (Date_Id, Store_Id, Product_Id).

  5. Dimension (data warehouse) - Wikipedia

    en.wikipedia.org/wiki/Dimension_(data_warehouse)

    A common data warehouse example involves sales as the measure, with customer and product as dimensions. In each sale a customer buys a product. The data can be sliced by removing all customers except for a group under study, and then diced by grouping by product. A dimensional data element is similar to a categorical variable in statistics.

  6. Snowflake schema - Wikipedia

    en.wikipedia.org/wiki/Snowflake_schema

    Normalization splits up data to avoid redundancy (duplication) by moving commonly repeating groups of data into new tables. Normalization therefore tends to increase the number of tables that need to be joined in order to perform a given query, but reduces the space required to hold the data and the number of places where it needs to be updated if the data changes.

  7. Dimensional modeling - Wikipedia

    en.wikipedia.org/wiki/Dimensional_modeling

    Extensibility. Dimensional models are scalable and easily accommodate unexpected new data. Existing tables can be changed in place either by simply adding new data rows into the table or executing SQL alter table commands. No queries or applications that sit on top of the data warehouse need to be reprogrammed to accommodate changes.

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    www.aol.com/woman-secretly-records-boyfriend...

    “Wait for the man who randomly tears up because he’s so in love," Madison Perrott wrote alongside the sweet clip of her boyfriend of over a year

  9. Fact constellation - Wikipedia

    en.wikipedia.org/wiki/Fact_constellation

    A fact constellation schema, also referred to as a galaxy schema, is a model using multiple fact tables and multiple dimension tables. [1] These schemas are implemented for complex data warehouses. [1] The fact constellation is a measure of online analytical processing and can be seen as an extension of the star schema.