The ever increasing availability of in situ, remote sensing and simulation data supports the development of data-driven alternatives to classical model-driven methods for the interpolation of sea surface geophysical fields from partial satellite-derived observations. In this respect, we recently introduced the Analog Data Assimilation (AnDA), which exploits patch-based analog forecasting operators within a classic Kalman-based data assimilation framework. In this work, we consider the application of AnDA to the spatio-temporal interpolation of SLA (Sea Level Anomalies) from two types of satellite altimetry data, namely from along-track nadir data and data from the upcoming wide-swath SWOT mission. We report a sensitivity analysis w.r.t. the main parameters of the proposed AnDA scheme. Overall, the reported benchmarking analysis supports the relevance of the proposed AnDA scheme for an improved reconstruction of mescoscale structures for horizontal scales ranging from ~20km to ~100km, with an gain of 42% (12%) in terms of SLA RMSE (correlation) with respect to Optimal Interpolation (OI). Results suggest an additional potential improvement from the joint assimilation of SWOT and along-track nadir observations.