Designing a control system that can robustly detect faulted emission control devices under any environmental and driving conditions is a very challenging task for any OEM. To gain confidence in the control strategy and the values of tunable parameters requires that the test vehicles are subjected to their limits during the development process. Complexity of modern powertrain systems along with the On-Board Diagnostic (OBD) monitors with multidimensional thresholds make it very difficult to anticipate all the possible worst case situations. To find the optimum solution of this problem in traditional calibration process can be very time and resource intensive. One possible solution is to take a data driven calibration approach. In this method, large amount of data is collected by collaboration of different groups working on same powertrain. Later the collected data is mined to find the optimum values of tunable parameters for respective vehicle functions. This large scale data (couple of terabytes) gives the engineers more samples to analyze and confidence in the calibration. A robust data analysis platform with the capability of handling large scale data and configurable enough to cater to different engineer’s need is required to accomplish this task. In this paper, we present some salient features of such a platform developed by Ford Motor Company. We highlight the memory efficiency, model in loop capabilities, and parallel processing capability of this platform. We also discuss the use MapReduce ideology in calibration data analysis paradigm.