The reconstruction of geophysical dynamics remain key challenges in ocean, atmosphere and climate sciences. Data assimilation methods are the state-of-theart techniques to reconstruct the space-time dynamics from noisy and partial observations. They typically involve multiple runs of an explicit dynamical model and may have severe operational limitations, including the computational complexity, the lack of model consistante with respect to the observed data as well as modeling uncertainties. Here, we demonstrate how large amount of historical satellite data can open new avenues to address data assimilation issues, and to develop a fully data-driven assimilation. Assuming that a representative catalog of historical state trajectories is available, the key idea is to use the analog method to propose forecasts with no online evaluation of any physical model. The combination of these analog forecasts with observations resorts to classical stochastic filtering methods. For illustration of the proposed analog data assimilation, the brute force use of 20 years of altimetric data is demonstrated to reconstruct mesoscale sea surface dynamics.