Key drivers or architectural goals for dimensional model

New analytical data modeling approach that can drastically lower technical complexity of enterprise analytics data platform, reduce development costs, shorten time to value and support agile, incremental development.

Dimensional modeling (also known as star or snowflake schema) was pioneered by Nielsen in 70s, and is considered the standard data modeling technique for business intelligence and analytics with nearly every BI ad reporting tool on the market supporting or requiring it.  Both leading data warehousing methodologies, Inmon’s Corporate Information Factory and Kimball’s dimensional modeling, prescribe to use a dimensional model for exposing data to users. The Dimensional model provides a logical grouping of data by reporting usage (facts/dimensions), delivers good performance, enables cross-system integration and supports MDM (master data management). Why are we challenging something that has been proven to work well for over 40 years? 

This whitepaper explores key drivers or architectural goals for dimensional models.

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