In this study we examine object storage, a cutting-edge cloud-native technology specifically designed for efficiently managing large datasets. While object storage offers significant cost-effectiveness compared to disk storage, it requires data to be appropriately adapted to fully realise its benefits. Data retrieval from object storage is over HTTP in complete "objects," which are either entire files or file chunks. As this is relatively new technology, there is a clear lack of established tools and best-practice for converting various file types for optimal use with object storage, particularly for large gridded and N-dimensional datasets used in environmental and climate science. The performance and speed of object storage are contingent upon the data's structure, chunking, and the specific analysis requirements of the user. Consequently, a better understanding of these interactions is essential before widespread adoption. To address this need, our study conducted a series of experiments using gridded data with different chunking strategies, aiming to identify the most efficient approach for utilizing and accessing data stored in an object store. Our findings highlight the need for comprehensive understanding of object storage before its widespread adoption, and serve as a valuable resource for guiding future users in utilizing object storage effectively
