The capacity of memory and storage devices is expected to increase drastically with adoption of the forthcoming memory and integration technologies. This is a welcome improvement especially for datacenter servers running modern data-intensive applications. Nonetheless, for such servers to fully benefit from the increasing capacity, the bandwidth of interconnects between processors and these devices must also increase proportionally, which becomes ever costlier under unabating physical constraints. As a promising alternative to tackle this challenge costeffectively, a heterogeneous computing paradigm referred to as near-data processing (NDP) has emerged. However, NDP has not yet been widely adopted by the industry because of significant gaps between existing software stacks and demanded ones for NDP-capable memory and storage devices. Aiming to overcome the gaps, we propose to turn memory and storage devices into familiar heterogeneous distributed computing systems. Then, we demonstrate potentials of such computing systems for existing data-intensive applications with two recently implemented NDPcapable devices. Finally, we conclude with a practical blueprint to exploit the NDP-based computing systems for speeding up solving future computer-aided design and optimization problems.