In data warehousing, the snowflake schema is a common data modeling technique that organizes data into a dimensional model for efficient querying and reporting. A snowflake schema consists of a central fact table surrounded by multiple dimension tables. However, one challenge that can arise when using the snowflake schema is dealing with degenerate dimensions.

What are Degenerate Dimensions?

Degenerate dimensions are a type of dimension that does not have its own dedicated dimension table. Instead, they are attributes of the fact table itself. These attributes are typically transaction-specific data such as order numbers, invoice numbers, or customer IDs.

Unlike regular dimensions, degenerate dimensions are not associated with any hierarchy or pre-defined set of values. They are simply used to identify or classify individual records in the fact table.

Handling Degenerate Dimensions in Snowflake Schema

When working with degenerate dimensions in a snowflake schema, there are a few approaches you can take to manage and utilize them effectively:

1. Include Degenerate Dimensions in the Fact Table

Since degenerate dimensions are directly associated with the fact table, you can include them as separate columns within the fact table itself. This allows you to easily join the degenerate dimension attributes when querying the fact table.

For example, if you have an order fact table, you can include the order number as a degenerate dimension directly in the fact table. This way, you can easily filter or group the data based on the order number.

2. Create a Virtual Dimension Table

If you find that a specific degenerate dimension has a high occurrence and needs to be used frequently, you can create a virtual dimension table for that attribute.

Virtual dimension tables are not physically stored in the database but can be created dynamically during queries using common table expressions (CTEs) or subqueries. This allows you to treat the degenerate dimension as if it were a regular dimension, enabling easier filtering, aggregations, and joining.

3. Use Snowflake’s Variant Data Type

Another option is to store degenerate dimensions as JSON-like documents using Snowflake’s VARIANT data type. This allows you to store multiple attributes within a single column and provides flexibility in handling different types of degenerate dimensions.

You can leverage Snowflake’s JSON functions to extract specific attributes or perform filtering based on the degenerate dimension values. This approach can be useful when dealing with varying or dynamic attributes within the degenerate dimensions.

Conclusion

Degenerate dimensions are a common occurrence in snowflake schema designs, as they represent transaction-specific information directly associated with the fact table. By including them in the fact table, creating virtual dimension tables, or using Snowflake’s VARIANT data type, you can effectively handle and utilize degenerate dimensions in your data warehousing solution.

Remember to adjust your data ingestion and query optimization strategies accordingly to achieve optimal performance.

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