Synthetic aperture radar (SAR) interferometer (InSAR) is now a key geodetic tool for monitoring the surface displacement. Thanks to ESA's Sentinel-1 sensors with IW mode as its default acquisition mode for land observations and its free access data policy, which have global coverage at moderate resolution with about 20m, national scale InSAR-based deformation is being studied in recent years by using big data techniques such as high performance computing and cloud computing. In this paper, we proposed the time series InSAR technique called Coherent-Scatterers InSAR (CS-InSAR) and its parallel solution for processing the whole CS-InSAR chain of Sentinel-1 data automatically and efficiently, considering the characteristics of CS-InSAR algorithm, such as frequent I/O data flow and heavy computation. By developing the parallelized CS-InSAR algorithm on the Big Earth Data Platform, 11922 satellite SAR data from September 2018 to December 2019 over China were processed, and the preliminary national InSAR-based surface deformation mapping for 2018–2019 was produced, with the deformation accuracy better than 0.6 cm in urban area.