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  2. Dimension (data warehouse) - Wikipedia

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

    Dimension table rows are uniquely identified by a single key field. It is recommended that the key field be a simple integer because a key value is meaningless, used only for joining fields between the fact and dimension tables. Dimension tables often use primary keys that are also surrogate keys.

  3. Early-arriving fact - Wikipedia

    en.wikipedia.org/wiki/Early-arriving_fact

    In the data warehouse practice of extract, transform, load (ETL), an early fact or early-arriving fact, [1] also known as late-arriving dimension or late-arriving data, [2] denotes the detection of a dimensional natural key during fact table source loading, prior to the assignment of a corresponding primary key or surrogate key in the dimension table.

  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. MultiDimensional eXpressions - Wikipedia

    en.wikipedia.org/wiki/MultiDimensional_eXpressions

    In this example, the query defines the following result set information The SELECT clause sets the query axes as the Store Sales member of the Measures dimension, and the 2002 and 2003 members of the Date dimension. The FROM clause indicates that the data source is the Sales cube.

  6. 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 ...

  7. Slowly changing dimension - Wikipedia

    en.wikipedia.org/wiki/Slowly_changing_dimension

    In data management and data warehousing, a slowly changing dimension (SCD) is a dimension that stores data which, while generally stable, may change over time, often in an unpredictable manner. [1] This contrasts with a rapidly changing dimension , such as transactional parameters like customer ID, product ID, quantity, and price, which undergo ...

  8. Dimensional fact model - Wikipedia

    en.wikipedia.org/wiki/Dimensional_fact_model

    For example, each sale is measured by the number of units sold, the unit price, and the total receipts. A dimension is a property, with a finite domain, that describes an analysis coordinate of the fact. A fact generally has multiple dimensions that define its minimum representation granularity.

  9. 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]