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Dealing with Degenerate Dimensions

Definition

A degenerate dimension is a dimension that does not have its own physical table in the data warehouse. Instead, it is typically represented as an attribute of a fact table. Common examples include invoice numbers, order numbers, or transaction IDs.

Importance of Degenerate Dimensions

Understanding degenerate dimensions is crucial for several reasons:

  • They help to identify and analyze transactional data.
  • They reduce complexity by avoiding unnecessary tables in the schema.
  • They provide context for fact data, allowing for better analytics.

Handling Degenerate Dimensions

To effectively deal with degenerate dimensions, consider the following steps:

Step-by-Step Process


1. Identify potential degenerate dimensions from your transactional data.
2. Define the attributes that will be included in the degenerate dimension.
3. Integrate the degenerate dimension as an attribute within the fact table.
4. Ensure that your ETL processes are designed to handle the degenerate dimension correctly.
5. Validate and test the data for accuracy and completeness.
                

Best Practices

Follow these best practices to effectively manage degenerate dimensions:

  • Keep your fact tables clean and focused.
  • Document the usage of degenerate dimensions for future reference.
  • Regularly review and optimize your data model.
  • Utilize visualization tools to analyze the impacts of degenerate dimensions.
  • Train your analytics team on the implications of using degenerate dimensions.

FAQ

What is an example of a degenerate dimension?

An invoice number is a common example, as it represents transactional data without needing a separate table.

How do degenerate dimensions affect performance?

Degenerate dimensions can improve performance by reducing joins and simplifying the data model, but can also complicate queries if not managed properly.

When should I avoid using degenerate dimensions?

Avoid using them when the dimension data has significant attributes that warrant their own table for better normalization and usability.