What is a Snowflake Schema with Databases and when to use? CompTIA Data Plus Certification Prep.Nov 07, 2023
A snowflake schema is a logical arrangement of tables in a multidimensional database such that the entity relationship diagram resembles a snowflake shape.
It is an extension of the star schema, where dimension tables are broken down into subdimensions. Snowflake schemas are commonly used for business intelligence and reporting in OLAP data warehouses, data marts, and relational databases.
Characteristics of a Snowflake Schema
Centralized fact table: Like a star schema, a snowflake schema has a central fact table that contains the core transactional data.
Normalized dimension tables: Unlike a star schema, where dimension tables are denormalized to improve query performance, snowflake schemas normalize dimension tables by breaking them down into subdimensions. This normalization helps to reduce data redundancy and improve data integrity.
Hierarchical relationships: Snowflake schemas can represent complex hierarchical relationships between dimension tables. This is achieved by using foreign keys to link subdimension tables to their parent dimension tables.
Advantages of a Snowflake Schema
Improved data integrity: Normalization reduces data redundancy and improves data consistency.
Reduced data anomalies: Normalization helps to eliminate data anomalies, which are inconsistencies in the data.
Simplified data management: Normalized data is easier to understand and manage, which can reduce the cost of maintaining the database.
Disadvantages of a Snowflake Schema
Increased query complexity: Normalized dimension tables can make queries more complex, which can impact query performance.
Increased storage requirements: Normalization can increase the storage requirements of the database.
When to Use a Snowflake Schema
Snowflake schemas are a good choice for databases that need to represent complex relationships between data entities and where data integrity is a priority. They are also a good choice for databases that need to support complex queries. However, snowflake schemas can be more difficult to manage than star schemas, and they may not be as efficient for ad-hoc queries.
Here is an example of a snowflake schema:
Fact Table: Sales SalesID | CustomerID | ProductID | OrderDate | Quantity | SalesAmount Dimension Tables: Customers CustomerID | FirstName | LastName | Email Products ProductID | ProductName | ProductCategory | UnitPrice OrderDates OrderDate | Day | Month | Year
In this example, the Sales fact table is connected to three dimension tables: Customers, Products, and OrderDates. The Customers dimension table is normalized into two subdimension tables: FirstName and LastName. The OrderDates dimension table is normalized into three subdimension tables: Day, Month, and Year.
The CompTIA Data+ exam is an entry-level certification for aspiring data analysts. It is designed for individuals who want to demonstrate their ability to collect, analyze, and interpret data to inform business decisions.
I hope this blog post has convinced you of the value of the CompTIA Data+ certification exam.
If you are interested in a career in data, I encourage you to take the exam and see how it can help you achieve your goals.
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