Edge computing presents a promising paradigm for the management and processing of the vast volumes of data generated by Internet of Things (IoT) devices. By merging cloud services with decentralized processing at the edge of the network, edge computing optimizes resource utilization while mitigating communication overhead and data transfer delays. Despite advancements, there are issues regarding cloud/edge-based application requirements. A distributed edge storage solution is crucial, ensuring data proximity, minimizing network congestion, and adapting to changing demands. Nevertheless, implementing or selecting an efficient edge-enabled storage system presents numerous challenges due to the distributed and heterogeneous nature of the edge, as well as its limited resource capabilities. Hence, it is essential for the research community to actively contribute towards clarifying the objectives and delineating the strengths and weaknesses of different storage solutions. This work presents an overview and performance analysis of three storage solutions in the edge computing context, namely MinIO, IPFS, and BigchainDB. The evaluation considers a set of Quality of Service (QoS) and resource utilization metrics. The systems are deployed on a cluster of four Raspberry Pis, which function as a network of edge devices. The results demonstrate the superiority of IPFS and provide insights into the performance of the evaluated storage systems for edge deployments.