Data Mining and Data Warehousing - IT 446

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Data Mining and Data Warehousing
IT 446
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Pg. 1 Question TwoQuestion Two
Question One
Compare and contrast Star and Snowflakes schema of data warehousing model.
Star Schema: In terms of star schema, one fact table can remain in the center of
star and other linked dimension tables around it. This schema is called star
schema as the structure is similar to a star (Sanchez, 2016). Star schema is one
of the simplest type of schema in data warehousing. It has another name called
star join schema. This schema is suitable for querying large data sets.
Example of Star Schema
2 MarksLearning
Outcome(s):1
LO1: Explain
different data
mining tasks,
problems and the
algorithms most
appropriate for
addressing them.

Pg. 2 Question TwoQuestion Two
Star schema represents every dimension using single dimension table. Attributes of
dimension should be represented within dimension table. Each dimension table should
be connected to fact table using foreign key. Dimension tables should not have
connection between each other (Sidi et al., 2016). Fact table should consist of measure
and key. Star schema is well known for optimal disk space and easily understandable
concepts. Normalization concepts should not be applied to dimension tables. Star
schema has wide support by business intelligence tools.
Snowflake Schema: Snowflake is the extended version of star schema. This is because
snowflake includes additional dimensions within star schema. The same reason is
applied to naming this schema, its structure is similar to a snowflake. Snowflake tables
are created by applying normalization concepts (Benjelloun, El Merouani & El Amin,
2017). For this reason, the dimension tables are decomposed into smaller tables which
add additional dimension tables into schema.
Snowflake Schema
The biggest feature of snowflake schema is lesser usage of disk space than star
schema. Dimension implementation is comparatively easier. Query performance is