Star Schema

The star schema is a data modeling method commonly used in data warehouses and business intelligence (BI) systems. This simple model organizes data around a central fact table, which is linked to dimension tables. This structure facilitates the creation of efficient SQL queries and enables fast analysis through BI tools optimized for querying and refreshing data that follows the star schema, such as Power BI. The star schema is ideal for decision support systems, where query simplicity and processing efficiency are essential. Queries are easier to write and execute, reducing complexity and minimizing errors in reporting.

However, the star schema has some limitations. The lack of systematic normalization can lead to data redundancy in dimension tables, increasing storage volume. Additionally, business rule changes can be difficult to implement, requiring complex adjustments to fact and dimension tables.

Alternatives to the star schema include:

• Data Vault: More flexible for handling frequent changes in business rules.
• One Big Table: Simplifies data access but can lead to complex maintenance operations when data structures change.

Theodo’s point of view

We recommend the star schema and dimensional modeling in general for structuring complex data at scale. They are flexible and proven approaches. However, other models may be preferable depending on the use case, such as Data Vault or One Big Table for analyzing large IoT datasets that lack clear dimensions.