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  2. Dimension (data warehouse) - Wikipedia

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

    Type 1 (Overwrite): Old values are overwritten with new values for attribute. No history. Type 2 (Add new row): A new row is created with either a start date / end date or a version for a new value. This creates history. Type 3 (Add new attribute): A new column is created for a new value. History is limited to the number of columns designated ...

  3. Snowflake schema - Wikipedia

    en.wikipedia.org/wiki/Snowflake_schema

    The snowflake schema is similar to the star schema. However, in the snowflake schema, dimensions are normalized into multiple related tables, whereas the star schema's dimensions are denormalized with each dimension represented by a single table. A complex snowflake shape emerges when the dimensions of a snowflake schema are elaborate, having ...

  4. 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 ...

  5. Entity–attribute–value model - Wikipedia

    en.wikipedia.org/wiki/Entity–attributevalue...

    In an EAV data model, each attributevalue pair is a fact describing an entity, and a row in an EAV table stores a single fact. EAV tables are often described as "long and skinny": "long" refers to the number of rows, "skinny" to the few columns. Data is recorded as three columns: The entity: the item being described. The attribute or ...

  6. Dimensional modeling - Wikipedia

    en.wikipedia.org/wiki/Dimensional_modeling

    The dimensions must be defined within the grain from the second step of the 4-step process. Dimensions are the foundation of the fact table, and is where the data for the fact table is collected. Typically dimensions are nouns like date, store, inventory etc. These dimensions are where all the data is stored.

  7. Star schema - Wikipedia

    en.wikipedia.org/wiki/Star_schema

    Dimensions can define a wide variety of characteristics, but some of the most common attributes defined by dimension tables include: Time dimension tables describe time at the lowest level of time granularity for which events are recorded in the star schema; Geography dimension tables describe location data, such as country, state, or city

  8. Dimensional fact model - Wikipedia

    en.wikipedia.org/wiki/Dimensional_fact_model

    A dimensional attribute is a property, with a finite domain, of a dimension. Like dimensions, a dimensional attribute is represented by a circle. For instance, a product may be described by its type, category, and brand; a customer may be represented by city and nation. The relationships among the dimensional attributes are expressed by ...

  9. Slowly changing dimension - Wikipedia

    en.wikipedia.org/wiki/Slowly_changing_dimension

    The type 5 slowly changing dimension allows the currently-assigned mini-dimension attribute values to be accessed along with the base dimension's others without linking through a fact table. Logically, we typically represent the base dimension and current mini-dimension profile outrigger as a single table in the presentation layer.