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  2. Snowflake schema - Wikipedia

    en.wikipedia.org/wiki/Snowflake_schema

    The snowflake schema is in the same family as the star schema logical model. In fact, the star schema is considered a special case of the snowflake schema. The snowflake schema provides some advantages over the star schema in certain situations, including: Some OLAP multidimensional database modeling tools are optimized for snowflake schemas. [3]

  3. Star schema - Wikipedia

    en.wikipedia.org/wiki/Star_schema

    The star schema is an important special case of the snowflake schema, and is more effective for handling simpler queries. [ 2 ] The star schema gets its name from the physical model's [ 3 ] resemblance to a star shape with a fact table at its center and the dimension tables surrounding it representing the star's points.

  4. Snowflake ID - Wikipedia

    en.wikipedia.org/wiki/Snowflake_ID

    A tweet produced by @Wikipedia in June 2022 [4] has the snowflake ID 1541815603606036480. The number may be converted to binary as 00 0001 0101 0110 0101 1010 0001 0001 1111 0110 0010 00|01 0111 1010|0000 0000 0000, with pipe symbols denoting the three parts of the ID. The first 41 (+ 1 top zero bit) bits convert to decimal as 367597485448.

  5. Fact table - Wikipedia

    en.wikipedia.org/wiki/Fact_table

    A transactional table is the most basic and fundamental. The grain associated with a transactional fact table is usually specified as "one row per line in a transaction", e.g., every line on a receipt. Typically a transactional fact table holds data of the most detailed level, causing it to have a great number of dimensions associated with it.

  6. Dimensional modeling - Wikipedia

    en.wikipedia.org/wiki/Dimensional_modeling

    This step is to identify the numeric facts that will populate each fact table row. This step is closely related to the business users of the system, since this is where they get access to data stored in the data warehouse. Therefore, most of the fact table rows are numerical, additive figures such as quantity or cost per unit, etc.

  7. Dimension (data warehouse) - Wikipedia

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

    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 for storing historical data.

  8. Snowflake Shares Soar as Outlook Brightens. Is It Too ... - AOL

    www.aol.com/snowflake-shares-soar-outlook...

    The Motley Fool has positions in and recommends Microsoft, ServiceNow, and Snowflake. The Motley Fool recommends the following options: long January 2026 $395 calls on Microsoft and short January ...

  9. Degenerate dimension - Wikipedia

    en.wikipedia.org/wiki/Degenerate_dimension

    According to Ralph Kimball, [1] in a data warehouse, a degenerate dimension is a dimension key (primary key for a dimension table) in the fact table that does not have its own dimension table, because all the interesting attributes have been placed in analytic dimensions.