The founders of WhereScape, based on more than a decade of experience building data warehousing and decision support systems environments for the world’s most demanding clientele, built WhereScape RED with a single idea in mind: produce a complete, pragmatic, methodologically aware toolset that allows data warehousing and database professionals to design, construct and operate data warehouses rapidly.

In the service of that objective, WhereScape’s designers focused on several key principles:

Dimensional models are a fixture of modern data warehouse design. Although some cutting-edge analytical environments are now largely or wholly automated, using code that can navigate complex normalized schema to perform analyses, the vast majority of data warehousing and decision support environments are used by human beings, manipulating data in commercial off-the-shelf query and reporting tools. Dimensional data models – also known as star schema -- are essential for these users, since these schema provide the inherent legibility and navigability that business decision-makers require to formulate questions quickly, and receive accurate answers from the warehouse.


Prototype-and-iterate cycles are inevitable, and essential, in the design process. Business decision-makers often cannot articulate precisely what data elements they need to make business decisions, either because they have never seen the data elements available to them, or because the artificial (and often rote) nature of requirements-gathering sessions fail to spark their interest and elicit meaningful responses. As such – and as Inmon predicted more than a decade ago – data warehousing designers inevitably have to build prototypes of warehousing environments in order to elicit substantive feedback from user communities, and often have to prototype-and-iterate three or four times in order to arrive at a design that is user-optimized and certain of acceptance by user communities.