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Difference between Star Schema and Snowflake Schema

The following article outlines the differences between Star Schema vs Snowflake Schema.

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Star Schema and Snowflake Schema are two common techniques for data modeling in data warehousing. The Star Schema has a central fact table and a set of dimension tables that are directly connected to it, forming a star-shaped structure. In contrast, the Snowflake Schema expands on the Star Schema by normalizing the dimension tables, creating a more complex snowflake-shaped structure. One of the potential drawbacks of Star Schema is that it can result in redundant and inconsistent data due to its denormalized structure. On the other hand, Snowflake Schema’s normalized structure can improve data consistency by reducing redundancy.

What is Star Schema?

Example:

Consider a refrigerator manufacturing company and create a schema for the sales of the company. Sales will have the following dimensions:

Item

Location

Branch

Time

The schema has a fact table at the center for sales, which would contain keys to associate with each dimension, having two measures, i.e., units sold and dollars sold.

What is Snowflake Schema?

Example:

Considering the same above example of the refrigerator manufacturing company, in the Snowflake Schema, the fact table is the same as in the Star Schema, but the major difference is in the definition or layout of dimension tables.

In this schema, the single dimension table of the item has been normalized and split, resulting in the creation of a new supplier table that includes information on the type of supplier. Likewise, the dimension table of location has been normalized, and its data has been split into a new city table that contains details of each specific city.

Star Schema vs Snowflake Schema: Head-to-Head Comparison (Infographics)

Below are the top 9 differences between Star Schema vs Snowflake Schema:

Star Schema vs Snowflake Schema: Key Differences

Here are some major differences between Star Schema vs Snowflake Schema:

Star Schema:

In a Star Schema, data analysts store hierarchies of dimension in a dimension table.

It contains a central fact table encircled by a dimension table.

In this, a single join associates the fact table with a dimension table.

It has a simple design.

The data structure is denormalized.

The query executes at a faster rate.

In this cube, the processing is faster.

It has more redundant data.

It uses simple queries.

Star Schema is easy to understand.

Higher consumption of space

Snowflake Schema:

In a Snowflake Schema, data analysts store hierarchies in separate tables.

Snowflake Schema includes a fact table surrounded by dimension tables, which are in turn surrounded by further dimension tables.

In this schema, many joins are necessary for fetching the data.

It has a complex design.

The data structure is normalized.

The query executes comparatively slower than the Star Schema.

In the Snowflake Schema, cube processing is slower.

It contains less redundant data.

It uses complex queries.

The Snowflake Schema is comparatively more difficult to understand than the Star Schema.

Less space usage in the Snowflake Schema.

Star Schema vs Snowflake Schema: Comparison Table

The following are the comparisons between Star Schema vs Snowflake Schema:

Characteristic

Star Schema

Snowflake Schema

Maintenance/Change It has more redundant data, and hence it is more difficult to change or maintain. This schema is easier to change and maintain due to less redundancy.

Understandability The complexity of the query is less and hence easy to understand. Queries applied are more complex and hence difficult to understand.

Query Execution Time It has fewer foreign keys, and hence the query execution is faster and takes lesser time. Due to more foreign keys, the query execution time is more, or the query executes slowly.

Type of Data Warehouse Better for datamarts having single relationship, i.e. one to one, or one to many Better for complex relationships, i.e. many to many relationships

Number of Joins It has more number of joins It has less number of joins

Dimension Table It has only one dimension table for each dimension. It has one or more dimension tables for a single dimension.

Usability Preference to the star schema when the dimension table has a smaller size, i.e., fewer rows. Good to use when the size of the dimension table is bigger.

Normalization and Denormalization Both the fact table and dimension tables are denormalized. A fact table is denormalized, while dimension table is normalized.

Data Model It follows a top-down approach. It follows a bottom-up approach.

Conclusion

Both the Star Schema and the Snowflake Schema represent data warehouses. They share similarities and differences. The Snowflake Schema is an extension of the Star Schema and is the primary preference when data is more abundant because it reduces redundancy. However, the Star Schema is still more popular than the Snowflake Schema