What are the key differences in snowflake and star schema? Where should they be applied?

What are the key differences in snowflake and star schema? Where should they be applied?

The Star schema vs Snowflake schema comparison brings forth four fundamental differences to the fore:

1. Data optimization: 

Snowflake model uses normalized data, i.e. the data is organized inside the database in order to eliminate redundancy and thus helps to reduce the amount of data. The hierarchy of the business and its dimensions are preserved in the data model through referential integrity.

Figure 1 – Snow flake model

Star model on the other hand uses de-normalized data. In the star model, dimensions directly refer to fact table and business hierarchy is not implemented via referential integrity between dimensions.

Figure 2 – Star model

2. Business model:

Primary key is a single unique key (data attribute) that is selected for a particular data. In the previous ‘advertiser’ example, the Advertiser_ID will be the primary key (business key) of a dimension table. The foreign key (referential attribute) is just a field in one table that matches a primary key of another dimension table. In our example, the Advertiser_ID could be a foreign key in Account_dimension.

In the snowflake model, the business hierarchy of data model is represented in a primary key – Foreign key relationship between the various dimension tables.

In the star model all required dimension-tables have only foreign keys in the fact tables.

3. Performance:

The third differentiator in this Star schema vs Snowflake schema face off is the performance of these models. The Snowflake model has higher number of joins between dimension table and then again the fact table and hence the performance is slower. For instance, if you want to know the Advertiser details, this model will ask for a lot of information such as the Advertiser Name, ID and address for which advertiser and account table needs to be joined with each other and then joined with fact table.

The Star model on the other hand has lesser joins between dimension tables and the facts table. In this model if you need information on the advertiser you will just have to join Advertiser dimension table with fact table.

Star schema explained

Star schema provides fast response to queries and forms the ideal source for cube structures. 

4. ETL

Snowflake model loads the data marts and hence the ETL job is more complex in design and cannot be parallelized as dependency model restricts it.

The Star model loads dimension table without dependency between dimensions and hence the ETL job is simpler and can achieve higher parallelism.

This brings us to the end of the Star schema vs Snowflake schema debate. But where exactly do these approaches make sense?

Where do the two methods fit in?

With the snowflake model, dimension analysis is easier. For example, ‘how many accounts or campaigns are online for a given Advertiser?’

The star schema model is useful for Metrics analysis, such as – ‘What is the revenue for a given customer?’