Snowflake Schema Offer Flexible Querying and higher Overall Performance

A lot of businessmen consider data modeling to be a dark art performed by the enterprise IT department that provides no tangible business benefits and is simply designed to make ordinary humans feel confused and mediocre. Unfortunately, numerous IT departments really cultivate this view and, for those that do, the data modeling that they do fails to deliver any concrete advantages - disregarding the jibber-jabber they chant about it. It doesn't need to be like this.

When done appropriately, data modeling can bring huge business gains to any firm, that include:

u  Better quality for a host of different business activities.

u  Easier access to that data.

u  Resilient information systems

u  Better distinguishing of products, profit and cost centers.

u  Removal of repetitive and unneeded data.

u  Diminished cost and enhanced incomes.

In this article, the discussion is on what is a snowflake in data modeling.

In order to decipher what is Snowflake modeling, you should first know about Multidimensional schema. Multidimensional schema is particularly devised to model data warehouse systems. The schema is formed to address the specific needs of extremely huge databases designed for the analytical purposes (OLAP).

There are two main types of Multidimensional schema:

u  Star Schema

u  Snowflake Schema

Star Schema : 

The star schema is the least complex kind of Data Warehouse schema. It is referred to as star schema since its structure looks like a star. In the Star schema, the focal point of the star can have one fact tables and numbers of related dimension tables. It is also called Star Join Schema and is optimized for querying huge data sets.

Snowflake Schema : 

A Snowflake Schema is an augmentation of a Star Schema, and it adds extra dimensions. It is called snowflake since its diagram looks like a Snowflake. The dimension tables are normalized which splits data into extra tables.

There are some major advantages of snowflake schema like:

u   There is a distinct improvement in query performance since the snowflake schema utilizes smaller disk space and connects lookup tables with smaller sizes.

u   The snowflake schema provides superior data quality (data is more organized, so data integrity issues are minimized).

u   Improves overall performance since smaller tables are coupled.

u   Bring higher flexibility for interrelationships between components and dimension levels.

u   Require zero maintenance due to zero redundancy.

Snowflake schema Offer Flexible Querying : 

A snowflake schema is designed for flexible querying over more complex relationships and dimensions. It is suitable for one-to-many and many-to-many relationships between dimensions levels and is normally associated with data marts and dimensional data warehouses, where information recovery speed is more important than information manipulation efficiency. Snowflake modeling schema is often used with cutting edge query tools, which build an abstraction layer among users and raw tables, for scenarios that have many queries with extensive specifications.

Conclusion : 

The advantage of snowflake schema is that minimal duplicate data is stored than in an equivalent star schema. Moreover, a snowflake schema is able to support queries on the dimension tables on a lower granularity level. For the query, to obtain the URL of a website title, a very small table need to be queried.