Current (driving direction) algorithms may include a previously gathered artifact (e.g., street's speed limit, traffic lights, latitude and longitude coordinates, and additionally elusive information) in order to determine an outcome (e.g., a best direction).  A standard structured query language (SQL) process may enable a similar decision tree in which statistics comprise a prominent role to formulate an optimal database access path or execution plan. 
Standard database statistical histograms provide vital information to a SQL optimizer in a relational database management system (RDBMS).  In order to gather statistical metadata, a partial table partition scan is necessary, thereby consuming time and hardware resources. 
In extract, transform, and load (ETL) systems, gathering statistics may be time consuming such that after loading a large table partition it may be necessary to gather the statistics to provide guidance to an optimizer which may take hours to complete.  For example, an optimal query might be run after the completion of a gathering statistics step which has taken place after a load partitioning step in a serialized manner. 
Accordingly, there exists a need in the art to overcome at least some of the deficiencies and limitations described herein above.