The field of key-value stores and distributed storage is witnessing significant advancements, driven by the need for efficient and scalable data management. Researchers are focusing on optimizing key-value stores to better integrate with applications, reducing semantic mismatches and improving performance. Hierarchical data management and schema-aware access are emerging as key strategies to enhance the efficiency of key-value stores. Additionally, distributed storage models are being improved to address the challenges of heterogeneity and reliability, with novel erasure coding and scheduling algorithms being proposed. Furthermore, advancements in storage technology, such as Non-Volatile Memory (NVM), are leading to the development of more efficient caching middleware designs. Noteworthy papers in this area include FOCUS, which introduces a hierarchical KV model for fine-grained data organization and schema-aware access, and D-Rex, which proposes dynamic scheduling algorithms for heterogeneity-aware reliability in distributed storage. BVLSM is also notable for its write-efficient LSM-Tree storage mechanism via WAL-time key-value separation. These innovations are poised to significantly impact the field, enabling more efficient and scalable data management systems.