What are the benefits and challenges of using degenerate dimensions in fact tables?

Benefits of degenerate dimensions in FACT tables include simplified data modeling, reduced join complexity, and improved query performance. Challenges may arise from limited flexibility in querying and potential redundancy in storing repeated values. Design degenerate dimensions thoughtfully, considering factors like data volatility and frequency of use. Examples include invoice numbers, order numbers, and transaction IDs. Best practices include using degenerate dimensions sparingly, ensuring they provide significant context, and optimizing storage by avoiding unnecessary duplication. Overall, degenerate dimensions offer efficiency gains but require careful consideration to avoid drawbacks and maintain data integrity.

Degenerate Dimensions(DD), also known as "Junk Dimension" are commonly seen in Transaction fact tables and Accumulation Snapshot fact table. Going in detailed about the benefits of DD, they potentially link back to the operational system. On the other hand, they play an integral role in fact table primary key. Moreover, as they sit in the fact table and does not require join to the dimension table, searching over DD will be fast. For example, in an Procurement Management System, in an accumulation snapshot fact, one can directly query the fact table over Purchase Order Number (PO_num) and get the status of it. On the other hand, in a transactional fact table, the purchase order number can be used to group the items underlying the PO_num.

Degenerate dimensions often represent IDs or codes that, by themselves, do not offer much analytical value. They are typically used for filtering, grouping, or tracing back to source systems but do not provide descriptive insights. But I don't deny the fact that they help simplify the structure of fact tables by eliminating the need for separate dimension tables for certain attributes. This is particularly useful for attributes that do not have descriptive information beyond their ID or code, such as transaction IDs, invoice numbers, or ticket IDs.

When you use a degenerate dimension, you can achieve a compact solution where you can view all the data without needing a semantic model. However, it's important to note that when you apply a filter in your dashboard, the background query scans the entire dataset rather than just the specific dimension. Then, you must have in your mind this topic, the performance.

The Degenerate Dimension in the fact table links back to the operational system. In a data warehouse environment, when data is ingested from multiple sources into OLAP environment. The main challenge when populating DD is to handle data quality and inconsistency issues that may occur when integrating data of multiple source systems. Care should be taken in the ETL process by maintaining cross-mapping tables to make sure of data standardization and data consistencies.

The way we have implemented degenerate dimension in a retail/POS set of tables is concatenation of: * Original txn_id coming from source * Numeric store number/ID * Business date * Data source Identifier Each of these are LPAD'd with the maximum length of each attribute. This is stored in a highly accommodative data type like decimal(38,0) We have had cases where the original txn_id coming from source might be alphanumeric, we have created a function that uses some hashing libraries to produce a numeric equivalent in deterministic manner. This helps us achieve uniqueness for a given transaction and keep it numeric for better join performance.

Benefits of Degenerate Dimensions: - Simplicity: Degenerate dimensions simplify the design of a fact table by eliminating the need for separate dimension tables for certain identifiers that don’t require related descriptive attributes. 📉 - Performance: By reducing the number of joins needed when querying the fact table, degenerate dimensions can improve query performance, making data retrieval faster and more efficient. ⏩ - Uniqueness: They provide a unique identifier within the fact table, which can be crucial for tracking individual transactions or events and for ensuring data integrity. 🔍 Challenges: - Limited Analytical Value📊 - Data Volume📚 - Complexity in ETL🔄