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Star schemas with MicroStrategy SQL Generation

Considerations for the use of star schemas with MicroStrategy SQL Generation


The primary characteristic of star schema is its use of dimension tables rather than single-attribute lookup tables. For example, a Time dimension in a star schema may be supported by a dimension table with the following structure:

DAY_IDDAY_DESCMONTH_IDMONTH_DESCQUARTER_IDQUARTER_DESCYEAR_ID

By contrast, a snowflake schema has a separate lookup table for each level of a dimension. In a fully normalized snowflake schema, each lookup table contains only its attribute's ID and description columns, and the ID of its parent to facilitate joins up the hierarchy. Lookup tables may also be partially denormalized, in which ID columns of all parents in the dimension are included, or fully denormalized, in which IDs and descriptions of all parents are included.

Fully normalized snowflake schema:

DAY_IDDAY_DESCMONTH_ID

MONTH_IDMONTH_DESCQUARTER_ID

QUARTER_IDQUARTER_DESCYEAR_ID

YEAR_ID

Partially denormalized snowflake schema:

DAY_IDDAY_DESCMONTH_IDQUARTER_IDYEAR_ID

MONTH_IDMONTH_DESCQUARTER_IDYEAR_ID

QUARTER_IDQUARTER_DESCYEAR_ID

YEAR_ID

Fully denormalized snowflake schema:

DAY_IDDAY_DESCMONTH_IDMONTH_DESCQUARTER_IDQUARTER_DESCYEAR_ID

MONTH_IDMONTH_DESCQUARTER_IDQUARTER_DESCYEAR_ID
QUARTER_IDQUARTER_DESCYEAR_ID

YEAR_ID

Star schemas and fully denormalized schemas minimize the number of table joins that must be performed in SQL queries. A lower number of joins generally translates into better query performance, at the cost of higher data redundancy.

MicroStrategy SQL Generation Engine can work with properly designed star schema warehouses, however there are restrictions. Not all functionality can be done with a star schema warehouse.
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  1. LavanyaDecember 9, 2021 at 9:50 PM

    Nice and good article. It is very useful for me to learn and understand easily. Thanks for sharing
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