How do you balance the trade-off between granularity and performance when creating junk dimensions?

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Creating junk dimensions is a common technique in dimensional modeling to reduce the number of foreign keys in a fact table and simplify queries. However, junk dimensions also have a downside: they can increase the size and complexity of the dimension table and affect the performance of the data warehouse. How do you balance the trade-off between granularity and performance when creating junk dimensions? In this article, you will learn some tips and best practices to design and implement junk dimensions effectively.

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1 What are junk dimensions?

A junk dimension is a dimension table that contains a combination of low-cardinality attributes that do not belong to any other dimension. For example, you might have a fact table that records sales transactions with attributes such as payment method, delivery status, customer type, and so on. Instead of creating separate dimension tables for each attribute, you can create a junk dimension that contains all the possible combinations of these attributes and link it to the fact table with a single foreign key. This way, you can avoid having too many foreign keys in the fact table and make your queries simpler and faster.

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Sagar Navroop

? Architect | ??????????-?????????????? | Technologist
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Junk dimensions, compact in nature, store low-cardinality attributes like flags or indicators in a data warehouse. However, excessive granularity can hinder query performance due to increased join complexity. Balancing granularity involves consolidating related attributes to reduce dimensionality without compromising query efficiency. Best practices include identifying and grouping related flags or indicators for consolidation. Further, dimensions should be appropriately sized for optimal query performance. Using indexing and partitioning techniques further enhances data access efficiency. Regular assessment and refinement of junk dimension design based on evolving business requirements is crucial.

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2 How do junk dimensions affect performance?

Junk dimensions can improve the performance of the fact table by reducing the number of joins and filters required to query the data. However, junk dimensions can also degrade the performance of the dimension table by increasing its size and complexity. The more attributes you include in the junk dimension, the more rows and columns it will have. This can make the dimension table harder to load, update, and index, and also consume more disk space and memory. Moreover, if the attributes in the junk dimension have a high degree of variability or change frequently, the dimension table can become very sparse and inefficient.

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Sudhansu Patra

Cloud Solution Architecture|10x Azure Certified |3x Databricks Certified |Data Arcitect |Data Engineer |Technical Project Manager|DW & BI Architect|Data Science, AI & ML Enthusiast|SAFe 5.1 Agilist |ExIBMer |ExHP
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Junk dim affect query performnace due to. 1. It increase the join complexcity. 2. It challenging to create efficient index due to low-cardinality values. 3. Optimizing queries that involve junk dimensions might be more complex. 4. Junk dimensions might require additional maintenance efforts, especially if new attributes need to be added or if the dimension grows unexpectedly.

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3 How to balance granularity and performance?

To balance the trade-off between granularity and performance when creating junk dimensions, you need to consider the cardinality and variability of the attributes, the frequency and complexity of the queries, and the size and scalability of the data warehouse. Attributes that have a high cardinality or variability should be placed in separate dimension tables or use other techniques such as degenerate dimensions or mini-dimensions. When analyzing business requirements and query patterns, include attributes that are frequently used in the junk dimension to facilitate queries and improve performance. However, avoid including too many rarely used attributes as they will add unnecessary overhead. Additionally, estimate the growth rate and storage capacity of the data warehouse to ensure that the junk dimension does not exceed limits or compromise performance. Monitor and optimize loading, updating, indexing processes of the dimension table and apply compression and partitioning techniques if needed.

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4 How to design and implement junk dimensions?

Designing and implementing a junk dimension effectively requires several steps. First, review the source data and the fact table to identify low-cardinality attributes that don’t belong to any other dimension. Exclude attributes with high cardinality or variability, as well as those that are not relevant or useful for analysis. Then, use a tool or script to generate all possible combinations of the attributes and assign a unique key to each combination. Include a default or unknown value for each attribute for missing or invalid data. Create a dimension table with columns for the key and the attributes and load the data from the generated combinations. Create indexes and constraints on the key column and apply compression and partitioning techniques if needed. Finally, add a foreign key column to the fact table that references the key column of the junk dimension table. Populate this foreign key column with corresponding key values from the junk dimension table based on matching attributes.

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5 How to test and maintain junk dimensions?

To test and maintain junk dimensions, you should validate the data quality and accuracy, monitor the performance and scalability, and update the junk dimension as needed. This includes checking that the data in the junk dimension table is complete, consistent, and correct, verifying that queries return expected results, measuring and monitoring performance and scalability of the table and data warehouse, identifying and resolving any issues or bottlenecs that affect performance or scalability, updating the table whenever there are changes or additions to attributes or their values, updating the fact table and foreign key column accordingly, and documenting and communicating any changes to users and stakeholders.

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Scott Gaffney

Business Intelligence Specialist focused on Cognos administration, development, team leadership, mentoring and training as well as dimensionally-modeled data warehouse design using Kimball and BEAM methodologies.
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Sorry, but I need to break this into multiple statements as there is only a 750 character limit. This could have fallen under the next section, but I am curious how it is best to maintain descriptions for codes that are in junk dimensions. The source data only has codes and we have gathered descriptions for some of the codes from various sources and placed in .CSV files. When the ETL runs, it looks in the .CSV file for the code and writes that code and description to the junk dimension table. Is there a best practice to handle keeping the descriptions for codes up to date when they are not supplied by the source data? Also, what happens if the same code has a different description based on what entity is providing the source data?

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Thomas G. S.

Senior Associate, Data Analysis with Social Impact Architects
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Junk dimensions are an enduring concept in Data Management, but they also have a hidden benefit that has only recently become apparent: large collections of random & disparate data dimensions can facilitate unsupervised machine-learning. By providing high-quality, structured data to a Data Science team capable of producing an unsupervised machine learning algorithm, new relationships and trends can be discovered. For example, an unsupervised ML program looking at purchase information in a junk dimension might discover derived demand between two unrelated products, or offer insights into customer behavior in-store after a digital marketing campaign. These benefits are compounded with more complex data, like pharmaceutical discoveries.

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Scott Gaffney

Business Intelligence Specialist focused on Cognos administration, development, team leadership, mentoring and training as well as dimensionally-modeled data warehouse design using Kimball and BEAM methodologies.
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For medical claims, a carrier has a code that with a certain description but another carrier may have the same code but a different description for that code. We cannot write one record in the .CSV file for the code with its description to satisfy the first carrier and write another record in the file for the same code and the description the second carrier has for it. The ETL would not know which code to write to the junk dimension table, thus which surrogate key from the junk dimension table to write to the fact table. There is already a carrier dimension table so I am not even sure writing the Carrier name in the .CSV file and using it as a lookup before looking for the code is appropriate, but it may be what needs done to resolve this.

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How do you balance the trade-off between granularity and performance when creating junk dimensions?

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1 What are junk dimensions?

2 How do junk dimensions affect performance?

3 How to balance granularity and performance?

4 How to design and implement junk dimensions?

5 How to test and maintain junk dimensions?

6 Here’s what else to consider

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