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  2. Fact table - Wikipedia

    en.wikipedia.org/wiki/Fact_table

    Determine the lowest level (granularity) of summary in a fact table (e.g. sales dollars). An alternative approach is the four-step design process described in Kimball: [ 1 ] select the business process, declare the grain, identify the dimensions, and identify the facts.

  3. Star schema - Wikipedia

    en.wikipedia.org/wiki/Star_schema

    Fact tables are designed to a low level of uniform detail (referred to as "granularity" or "grain"), meaning facts can record events at a very atomic level. This can result in the accumulation of a large number of records in a fact table over time. Fact tables are defined as one of three types:

  4. Dimension (data warehouse) - Wikipedia

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

    In data warehousing, a dimension table is one of the set of companion tables to a fact table. The fact table contains business facts (or measures), and foreign keys which refer to candidate keys (normally primary keys) in the dimension tables. Contrary to fact tables, dimension tables contain descriptive attributes (or fields) that are ...

  5. Slowly changing dimension - Wikipedia

    en.wikipedia.org/wiki/Slowly_changing_dimension

    Logically, we typically represent the base dimension and current mini-dimension profile outrigger as a single table in the presentation layer. The outrigger attributes should have distinct column names, like “Current Income Level,” to differentiate them from attributes in the mini-dimension linked to the fact table.

  6. Snowflake schema - Wikipedia

    en.wikipedia.org/wiki/Snowflake_schema

    The snowflake schema is represented by centralized fact tables which are connected to multiple dimensions. "Snowflaking" is a method of normalizing the dimension tables in a star schema. When it is completely normalized along all the dimension tables, the resultant structure resembles a snowflake with the fact table in the middle. The principle ...

  7. Online analytical processing - Wikipedia

    en.wikipedia.org/wiki/Online_analytical_processing

    The cube metadata is typically created from a star schema or snowflake schema or fact constellation of tables in a relational database. Measures are derived from the records in the fact table and dimensions are derived from the dimension tables. Each measure can be thought of as having a set of labels, or meta-data associated with it.

  8. Fact-check: Does Biden have 'the lowest approval rating of ...

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  9. Bloom's taxonomy - Wikipedia

    en.wikipedia.org/wiki/Bloom's_taxonomy

    There are five levels in the affective domain, moving through the lowest-order processes to the highest: Receiving: The lowest level; the student passively pays attention. Without this level, no learning can occur. Receiving is about the student's memory and recognition as well. Responding: The student actively participates in the learning process.