This paper proposes an enabling data parallel local learning methodology for handling large data regression through the Gaussian Process (GP) modeling paradigm. The proposed model achieves parallelism by employing a specialized compactly supported covariance function defined over spatially localized clusters. The associated load balancing constraints arising from data parallelism are satisfied using a novel greedy clustering algorithm, GeoClust producing balanced clusters localized in space. Further, the use of the proposed covariance function as a building block for GP models is shown to decompose the maximum likelihood estimation problem into smaller decoupled subproblems. The attendant benefits which include a significant reduction in training complexity, as well as sparse predictive models for the posterior mean and variance make the present scheme extremely attractive. Experimental investigations on real and synthetic data demonstrate that the current approach can consistently outperform the state-of-the-art Bayesian Committee Machine (BCM) which employs a random data partitioning strategy. Finally, extensive evaluations over a grid-based computational infrastructure using the NetSolve distributed computing system show that the present approach scales well with data and could potentially be used in large-scale data mining applications.