Abstract: According to one aspect, it is appreciated that it may be useful and particularly advantageous to provide a data generator that creates more realistic data for testing purposes, especially in data systems where large volumes of data are necessary. In one implementation, a data generator is provided that produces relationally consistent data for testing purposes. For instance, a synthetic data generation process may be performed that produces any number of relationally consistent data table structures. Further, in another implementation, generation of the data can be statistically influenced so that the data generated can take on the “look and feel” of production data. Also, data may be produced as needed, and its generation may be performed in parallel, depending on interdependencies in the data.

Abstract: According to one aspect, it is appreciated that it may be useful and particularly advantageous to provide a data generator that creates more realistic data for testing purposes, especially in data systems where large volumes of data are necessary. In one implementation, a data generator is provided that produces relationally consistent data for testing purposes. For instance, a synthetic data generation process may be performed that produces any number of relationally consistent data table structures. Further, in another implementation, generation of the data can be statistically influenced so that the data generated can take on the “look and feel” of production data. Also, data may be produced as needed, and its generation may be performed in parallel, depending on interdependencies in the data.

Abstract: A system and methods for organizing and querying data within a linear grid management system. Data having multiple dimensions is associated with physical locations, where a first dimension is associated with a node and a second dimension is associated with a data storage identifier of a memory storage device. The data may have a third dimension which provides a field for ordering data within the memory storage device. Metadata may be used to map a logical table to data stored in the memory storage device. The data query may be divided into multiple subqueries, wherein each subquery is related directly to one node associated with a data storage identifier related to a memory storage device. A preSQL and postSQL process may be generated to access an external database. A dispatcher may manage data subrequests and a node may generate a unique and efficient parsing process from the received data subrequest.