In this post, we will understand the difference between star schema and snowflake schema.
Hierarchies of dimensions are stored in a dimensional table.
It contains a fact table that is surrounded by dimension tables.
In this schema, a single join creates the relationship between a fact table and any dimension tables.
It is a simple database design.
It has high levels of data redundancy.
The processing of cube is quick.
A single dimension table contains the aggregated data.
It is a de-normalized data structure.
The queries run quickly in comparison to other schema.
It uses start join query optimization technique. Hence, the queries perform well.
Tables can be connected with multiple dimensions.
Hierarchies of dimensions are divided into separate tables.
One fact table is surrounded by a dimension table which in turn is surrounded by other dimension tables.
It requires multiple joins to fetch the data.
It has a complex database design.
It is a normalized data structure.
It has low-level data redundancy.
The data is split into different dimension tables.
The processing of cube is slow due to complex joins in the schema.
It can be represented using a centralized fact table, which may not be connected to multiple dimensions.