To me, development projects fail or succeed in the first few weeks. Once a project starts off in the wrong direction, momentum and expectations tend to prevent a return to the proper path. With today’s wealth of database options each addressing exciting new possibilities, the right choice for the application’s data foundation plays a large part in steering a project to success.
At this year’s Enterprise Data World conference, William Brooks showed the relations among different data modeling approaches, in effect detailing how to derive nine different model types from a detailed conceptual entity relationship model. Mr Brooks’ presentation hinted at a way to correctly frame up your data direction early on in a project, setting the stage for success.
According to his presentation, called “Symmetry in Modeling Approaches“, the different model types — relational, graph, dimensional, JSON, XML, and so on — all represent different perspectives on the same data relationships. Each suits a different application, like dimensional for reporting applications, data vault for data warehouses, graph databases for multi-layered search, and so on. However, if properly constructed they all map back in predictable and specific ways to a normalized entity-relationship model.
I and others write that ER modeling should be integral to requirements definition, but Mr. Brooks’ presentation implies that ER modeling can also serve as the basis for application architecture as well. Continue reading →