Most modern data stores tend to be distributed, to enable the scaling of the data across multiple instances of commodity hardware. Although this ensures a near unlimited potential for storage, the data itself is not always ideally partitioned, and the cost of a network round-trip may cause a degradation of end-user experience with respect to response latency. The problem being solved is bringing the data objects closer to the frequent sources of requests using a dynamic repartitioning algorithm. This is important if the objective is to mitigate the overhead of network latency, and especially so if the partitions are widely geo-distributed. The intention is to bring these features to an existing distributed key-value store product, Redis.