The traditional wisdom for designing database schemas is to use a design tool (typically based on a UML or ER model) to construct an initial data model for one's data and its instantiation as a collection of relational tables. Then applications are coded against this relational schema. When business circumstances change (as they do frequently) one should run the tool to produce a new data model and a new collection of tables.
If every algorithm looked like "map reduce" and all data naturally fit a single data store, solving Big Data problems would be straightforward. The real world, however, is not so simple. Most big data problems require complex analytics over data that is spread out among multiple data stores. Current technology could be force-fit to address these problems, but only by sacrificing programmer productivity.
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