The field of data storage and analytics is moving towards more efficient and flexible solutions. Recent developments have focused on improving the performance and scalability of storage systems, particularly in the context of large-scale datasets and complex analytical tasks. There is a growing interest in object-based storage systems, which facilitate column-oriented access and support in-storage execution of data reduction operators. Additionally, graph-based approaches are being explored for various applications, including reinforcement learning and routing optimization. Noteworthy papers in this area include OASIS, which proposes a novel object-based analytics storage system, and GeoLayer, which presents a geo-distributed graph storage framework. Other notable papers include Peekaboo, which introduces a new attack framework against dynamic searchable symmetric encryption, and Tiga, which presents a design for geo-replicated and scalable transactional databases. Membrane is also a notable paper, which proposes a cryptographic access control system for data lakes. These papers demonstrate the innovative and advancing nature of the field, with a focus on improving performance, scalability, and security.