When working with databases, it is important to create optimized data models if storage efficiency, performance and fast searching are the goals. Star vs Snowflake schema are the two most commonly used schema when modeling multidimensional data spaces.
Here you can understand how the star and snowflake schema differ from each other and which schema is best suited for a data warehouse application.
How Does A Star Schema Work?
The schema takes its name from the fact that the measuring tables on the outer sides are arranged in a star shape around the event table in the middle.
This data model does not focus on standardization, but on improving the efficiency of reading management. This has its own drawbacks, such as the possibility of discrepancies. There is usually a trade-off between performance and storage requirements. In the case of the star schema, the focus is on performance and therefore the resulting databases are more memory intensive.
Star Schema In Practice
In practice, the star schema is widely used in data warehouse applications. This is because the schema is simple and straightforward. It can be adapted or extended as needed to meet changing needs. This is not possible to the same extent as with Snowflake.
If necessary, the star shape can be extended to a snowflake shape. The transition between the two approaches is seamless. This requires the creation of new tables with independent attributes to form the third normal form (3NF). The star shape can be extended with additional event tables. In this case a so-called galaxy schema is created.
How Is The Snowflake Schema Organized?
An important difference between a star schema and a snowflake schema is that in the latter, each dimension of the pattern has its own table.
This avoids the redundancy inherent in the star schema. The result is more compact and better structured data sets. This is a trade-off between redundancy and complexity. Anomalies with the snowflake schema are avoided, but the data set is more complex.
Creating multidimensional arrays quickly leads to a highly branched, snowflake-like structure.
In this case, aggregation queries must be found to reconnect the resulting tables. A major drawback of the system is therefore the longer search time.
In practice, it is often not possible to distinguish clearly between the two approaches. This is due to the large number of variants that exist for both models.
A Comparison Between Star and Snowflake Schema
Snowflake can save some extra space in the database, especially if the measurements have very large text fields. In general, however, the size of the event tables is such that they exceed the size of the measurement tables.
The advantage of the simplicity of the star schema is that fewer connections are needed when creating and maintaining the database. However, there are cases where a “snowflake” scheme is needed.
Some OLAP reporting tools work more efficiently with a snowflake schema.
In addition, the availability of multi-level relationships in a snowflake structure can simplify aggregation.
Which Schema Should You Pick For Data Warehouse Applications?
Practitioners wonder which schema is best suited for analytics tasks in the data warehouse. If you want to simplify SQL queries, you should definitely prefer the star schema. Since there are fewer foreign keys, queries can be executed faster. Overall, the structure is simpler and therefore more user-friendly.
If you have problems with duplicate data in your analyses, the snowflake schema may be a better choice. This is because this model is normalized. However, in SQL queries, it has to bear the disadvantages of large concatenated strings.
What Is The Role Of OLAP In This Context?
Those who do trend analysis or sales forecasting prefer to use OLAP modeling. The advantages include the fact that it requires little work to selectively query data and execute queries. Web analytics allows you to look at data from different perspectives. Data warehouses optimized for data analytics are therefore often based on OLAP processing.
To use OLAP, data must be available in a multidimensional structure, for which star and snowflake schema have been developed. OPAL is therefore closely linked to these models.
Generally speaking, the star schema is a good model to use if you want a standardized and localized data model that is easy to follow. If you are working with one event table and going through several dimension tables, this schema is the right choice.
Since the star schema is suitable for Power BI, this approach is the one to use when working with linked report views. This is because Power BI models are based on fast queries that support the star schema.
On the other hand, if you need a more specific schema for your modeling task, the snowflake approach may be more appropriate. This schema has advantages in data analysis, especially when dealing with multiple entities and relationships. As always, it all depends on the priorities and needs for which you choose the type of schema that fits you best.
Contact our data warehouse consultant to get even more information about the star schema and snowflake schema to multiply the benefits for your business.