Degenerate Dimension

What is Degenerate Dimension?

Degenerate Dimension, often termed as a junk dimension, is a type of dimension table in a star schema of a data warehouse. It does not have its own dimension table, but resides in the fact table as a primary key. Predominantly, it is used for storing transaction control values, invoice numbers, or other numeric values that are useful for tracking, but do not link to other dimensions.

Functionality and Features

The primary function of a degenerate dimension is to assist in transaction tracking and streamline data analytics. The key features of a degenerate dimension are:

  • It helps in categorizing facts in the fact table.
  • It provides a way to track the sequence of events in a transaction.
  • It facilitates quicker data retrieval due to residing in fact table.

Benefits and Use Cases

There are a plethora of benefits offered by Degenerate Dimension:

  • Reduced Complexity: The dimension being part of the fact table cuts down the need for joining tables which simplifies queries.
  • Effective Tracking: With the help of Degenerate Dimension, tracking the sequence of events or transactions becomes easier.
  • Speed: As it resides in the fact table, data retrieval is quicker which is crucial for time-sensitive analytics.

One primary use case of Degenerate Dimension is in retail sales, where invoice numbers act as a degenerate dimension to track each unique transaction.

Challenges and Limitations

While Degenerate Dimension proves beneficial in several contexts, it's not devoid of limitations:

  • As it lacks a separate dimension table, it does not have additional descriptive information.
  • In cases of extremely large transaction volume, the Degenerate Dimension fields can result in performance issues due to high cardinality.

Integration with Data Lakehouse

In a Data Lakehouse setup, where a blend of structured and unstructured data are processed, degenerate dimensions play an integral role in delivering faster insights by reducing the retrieval time of fact data. They can simplify the data model and enhance the performance of analytics queries, making them highly compatible with the concepts of a data lakehouse.


Performance is a notable feature when it comes to Degenerate Dimensions. Because they reside within the fact table, they facilitate quicker data retrieval, which is crucial during data analysis and decision making. However, care must be taken to handle high cardinality that might affect performance.


What is a Degenerate Dimension? It is a type of dimension that resides in a fact table of a data warehouse, helping in tracking sequence of events and categorizing facts.

What are the benefits of Degenerate Dimension? It offers reduced complexity in queries, effective tracking of transactions, and quicker data retrieval.

What are the limitations of Degenerate Dimension? It lacks additional descriptive information and in the case of high transaction volume, can result in performance issues due to high cardinality.

How does Degenerate Dimension integrate with a Data Lakehouse environment? They simplify the data model and enhance the performance of analytics queries in a Data Lakehouse setup.


Fact Table: In data warehousing, a fact table is a primary table in a dimensional model. It contains measurable and quantitative data.

Dimension Table: A table in a star schema of a data warehouse. Dimension tables store fields that contain descriptive attributes of data objects, providing context.

Data Lakehouse: A hybrid data management platform that combines the benefits of data lakes and data warehouses.

Star Schema: The simplest form of a dimensional model, in which data is organized into facts and dimensions. It's widely used for data warehousing and business intelligence.

Data Warehousing: A system used for reporting and data analysis, considered a core component of business intelligence.

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