As we've already seen, junk dimensions are built from groups of attributes that don't belong on any other dimension, generally columns from fact tables that represent flags or status indicators. When designing an Analysis Services solution, it can be quite tempting to turn each of these columns into their own dimension, having just one attribute, but from a manageability and usability point of view, creating a single junk dimension is preferable to cluttering up your cube with lots of rarely-used dimensions. Creating a junk dimension can be important for query performance too. Typically, when creating a junk dimension, we create a dimension table containing only the combinations of attribute values that actually exist in the fact table—usually a much smaller number of combinations than the theoretical maximum, because there are often dependencies between these attributes, and knowing these combinations in advance can greatly improve the performance of MDX queries...
Expert Cube Development with SSAS Multidimensional Models
Expert Cube Development with SSAS Multidimensional Models
Overview of this book
Table of Contents (19 chapters)
Expert Cube Development with SSAS Multidimensional Models
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Designing the Data Warehouse for Analysis Services
Building Basic Dimensions and Cubes
Designing More Complex Dimensions
Measures and Measure Groups
Handling Transactional-Level Data
Adding Calculations to the Cube
Adding Currency Conversion
Query Performance Tuning
Securing the Cube
Going in Production
Monitoring Cube Performance and Usage
DAX Query Support
Index
Customer Reviews