The ever increasing geophysical data streams pouring from earth observation satellite missions and numerical simulations along with the development of dedicated big data infrastructure advocate for truly exploiting the potential of these datasets, through novel data-driven strategies, to deliver enhanced satellite-derived geophysical products from partial satellite observations. We here demonstrate a proof-of-concept of the analog data assimilation for an application to the reconstruction of cloud-free level-4 gridded Sea Surface Temperature (SST) fields. Our results point out the relevance of big-data-oriented analog strategies to benefit from large-scale observation and/or simulation datasets for enhanced satellite-derived geophysical products.