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James Serra's Blog

James is currently a Senior Business Intelligence Architect/Developer and has over 20 years of IT experience. James started his career as a software developer, then became a DBA 12 years ago, and for the last five years he has been working extensively with Business Intelligence using the SQL Server BI stack (SSAS, SSRS, and SSIS). James has been at times a permanent employee, consultant, contractor, and owner of his own business. All these experiences along with continuous learning has helped James to develop many successful data warehouse and BI projects. James has earned the MCITP Business Developer 2008, MCITP Database Administrator 2008, and MCITP Database Developer 2008, and has a Bachelor of Science degree in Computer Engineering. His blog is at .

Junk dimensions

Junk dimensions are dimensions that contain miscellaneous data such as flags and indicators.  When designing a data warehouse, you might come across a source system that has a bunch of yes/no indicator fields.  If those fields needs to be tracked in a fact table, the result could be many small dimension tables (each with just a few rows) along with much more information stored in the fact table, causing performance issues.

Instead, use a junk dimension that holds all the unique combinations of those indicator fields into a single dimension and assigns a unique key.  This key is what is stored in the fact table.  So you will have only one additional dimension table and will reduce the number of fields in the fact table.  A key consideration when forming junk dimensions is how many combinations exist.  If the number of combinations is too high the junk dimensions size may be unmanageable, in which case you might want to have more than one junk dimension.

More info:

Kimball Design Tip #48: De-Clutter With Junk (Dimensions)

Design Tip #113 Creating, Using, and Maintaining Junk Dimensions

Data Warehousing: Junk Dimensions

Mystery or Junk data warehouse dimensions

Junk Dimension

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