With varying applications of Artificial Intelligence (AI) on an expanding global scale, performance metrics and standards of platforms have steadily elevated. High-Performance Edge Computing (HPEC) plays an instrumental role in lifting a substantial load on cloud computing for Deep Learning (DL). Notwithstanding, the collection, pre-and-post processing of big data engenders many challenges and opportunities for optimizing HPEC performance for data classification and, in turn, yields better outcomes. This is where the concept of data lakes is employed, which are raw-formatted large masses of data that are plausibly more compatible with many algorithms than structured data stores. Therefore, in this work, we carry out a comparative study that examines the merits, performance and efficiency metrics, and limitations of two notable HPEC platforms centered around a DL data lake framework. With classification accuracy of ~90%, results show that adequate performance and impressive computational efficiency, as fast as 8.19 msec per classified GAF image is achieved on the Jetson Nano.