This article examines how data access to large, distributed geospatial datasets can be significantly improved using Python and the OPeNDAP protocol in modern cloud environments. As organizations such as NASA migrate vast archives to object storage, challenges arise from the continued use of metadata-rich, hierarchical formats like HDF and NetCDF, which were not originally designed for efficient remote access over HTTP or S3. Rather than duplicating datasets into cloud-native formats, the work emphasizes the importance of interoperable, subset-driven access methods that minimize data movement and reduce costs. The paper focuses on OPeNDAP’s DAP4 protocol as a scalable solution for remote data access, enabling users to retrieve only the data relevant to their analysis through constraint expressions. It details how Python tools—particularly Xarray and Pydap—interact with OPeNDAP servers to create lazy metadata representations of remote datasets and stream binary data efficiently. While Xarray provides a powerful, user-friendly interface with parallel processing capabilities via Dask, the study highlights performance tradeoffs and the need for careful configuration when working with distributed data. Through technical analysis and benchmarking across diverse NASA datasets, the article demonstrates that performance gains depend critically on reducing the number of remote requests, leveraging server-side subsetting, and using constraint expressions to limit variables and dimensions prior to download. Results show that direct Pydap-based workflows often outperform Xarray aggregation in high-dimensional or multi-file scenarios, while optimized Xarray configurations can achieve comparable performance. Overall, the work provides practical guidelines and best practices for researchers seeking to efficiently access and analyze large-scale remote datasets, illustrating how combining OPeNDAP’s protocol capabilities with modern Python tools enables faster, more scalable, and cost-effective scientific workflows.