Abstract: Systems and methods for providing structured query language (SQL) access to non-relational data stores are described. SQL access can be provided to a non-relational data store in one embodiment by mapping storage attributes of the data store to one or more virtual relational tables. The virtual tables can include a schema representation of data stored in a non-relational data store without actually including the data in the virtual tables. In some embodiments, these systems and methods therefore advantageously merge certain benefits of relational database architectures with the scalability benefits of certain non-relational data store architectures. Certain features of the systems and methods described herein can also be used with existing relational databases. For example, the systems and methods described herein can perform join or similar operations between a relational table in one data store and a data object in another data store.

Abstract: Disclosed are systems and methods that allow a database engine to access a non-relational document database and dynamically apply a schema that maps documents comprising semi-structured data into a relational data structure. In embodiments, mapping may be accomplished by parsing and analyzing a set of sample documents to empirically generate a normalized tabular schema. In embodiments, the schema accommodates format evolution and variability of documents having different structures and different sets of attributes is automatically executed in a background process without requiring user involvement. This normalization process eliminates the need to create unique modeling languages for semi-structured data. In embodiments, an SQL dialect may be used to join query results and combine the resulting document data with tables from different dimensional or relational databases to generate a combined result without the need to create additional temporary tables for non-conforming documents.