In this chapter, we've learned a bit about the theory of data warehouse and data mart design, and how it should be applied when we're using Analysis Services. We've found out that we definitely do need to have a data mart designed according to the principles of dimensional modeling, and that a star schema is preferable to a snowflake schema; we've also seen how certain common design problems such as Slowly Changing Dimensions, junk dimensions, and degenerate dimensions can be solved in a way that is appropriate for Analysis Services. Last of all, we've recommended the use of a layer of simple views between the tables in the data mart and Analysis Services to allow us to perform calculations, change column names and join tables, and we've found out why it's better to do this than do the same thing in the Data Source View.
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