In terms of picking the right database schema for your data warehouse, everything ultimately boils down to choosing between the two most popular options. Of course, we are talking about the star schema or the snowflake schema.
When it comes to these schemas, you are going to discover that there is no singular choice. There is no guarantee that one should always be chosen over the other. There are several differences that serve to highlight just how distinctive these elements are. At the end of the day, you are going to have to choose for yourself. This can be done by studying the difference between star and snowflake schema.
Defining Star Schema
With a star schema, there are several things that you are going to want to note. You can begin with the design. The star schema features a single fact table that is furthermore connected to additional dimension tables. The overall impression is like that of a star, hence the name. With the star schema, you will want to note that there is only one join to create the interaction between the fact table and the dimension tables. One thing about the star schema that you will definitely want to note is that with star schemas, you’re talking about something that may feature a significant amount of redundancy. For some, maintaining or changing a star schema is something that can prove to be highly difficult indeed.
While the star schema does feature a certain measure of redundancy, it is also worth keeping in mind that this schema has a phenomenal ease-of-use. This is due to a low query complexity.
Another nice feature to the star schema that is worth keeping in mind concerns the query performance. Due to the fact that the star schema has a smaller number of foreign keys, the query execution time is going to be quite short. The star schema also utilizes a top/down approach, and it is ideal for any data mart that features a simple relationship, such as 1:1.
Defining Snowflake Schema
As is the case with a star schema, you will want to note that there are many unique features to the snowflake schema. As you begin to learn more about the snowflake schema, you should also begin to see some of the differences between a snowflake schema and a star schema.
To begin with, the snowflake schema is actually an extension of the star schema. The design features massive dimension tables that have been normalized into a multitude of tables. When the dimension table starts to become bigger, you will begin to see the transformation into the snowflake schema. Furthermore, you will want to be aware of the fact that a snowflake schemas utilizes several joins to connect the fact table to the dimension tables. You will also want to note that each dimension table is connected to a sub-dimensional table.
One of the more intriguing elements to the snowflake schema is the fact that it does not have redundancy whatsoever. This makes the snowflake schema not only easier to change, but it also makes the snowflake schema easier to maintain, as well. At the same time, you will also need to keep in mind that the snowflake schema tends to be more difficult for users to understand. This can likely be attributed to the fact that a snowflake schema has very complex queries.
At the same time, it can prove to be absolutely essential for certain types of data warehouses. For example, if you needed a schema that would give you the ability to break down highly complex relationships into fairly simple terms, then you are going to want to go with the snowflake schema.
What Are The Differences Between A Snowflake Schema And A Star Schema?
As you can gather from all of the information mentioned above, there are several differences between a star schema and snowflake schema that are worth keeping in mind. In the first place, you are going to notice that one is considerably more suited for simple tasks than the other. In other words, if you are dealing in simple relationships with your data warehouse, then you are going to want to go with the star schema.
On the other hand, if you need to define deeply complex relationships in the simplest terms possible, your best bet will be in the form of the snowflake schema. Within this specific difference, you can begin to see several more differences.
Basically, the star schema is extremely de-normalized. By comparison, you are going to find that the snowflake schema is quite normalized. Memory utilization is another fact that establishes a stark difference between these two entities. If you are concerned about memory utilization, then you are perhaps best suited for a snowflake schema. If not, then you shouldn’t have any troubles whatsoever with putting the star schema to work.
