## 5 Discussion
### Potential and Challenges of Big Data  
The above results and geological interpretation demonstrate how the paradigm of InSAR studies is rapidly changing not only with the advent of SAR big data, but also owing to the accessibility of cloud/grid infrastructure and chains to process such huge datasets. The nearly 300 images per single observation geometry that were processed to cover the study area of this paper well represent the typical big SAR stack that a satellite constellation such as Sentinel-1 is already able to provide, now that its two twin satellites have been collecting images globally for more than six years, with a site revisit of up to six days. Even if the site revisit was constrained to 12 days only (as it currently is in geographic areas outside Europe; [9]), the constellation would still collect 30 images per year. To put this number into context, this exceeds the typical minimum amount of C-band SAR images per single observation geometry that InSAR practitioners consider as the minimum requirement to carry out a meaningful and reliable PSI processing (i.e., 15-20 scenes; [5]), regardless of the total period of observation. The latter plays a crucial role. Relatively short time periods might be unable to capture the long-term behavior of the deformation processes of interest, and/or be biased by the occurrence of annual fluctuations or other short-term dynamics that could influence the estimation of deformation rates. Therefore, the use of Sentinel-1 imagery to investigate \"silent\" and subtly evolving geohazards that manifest over long periods and with trends that can be captured over years (such as the slow-moving landslides and coastal subsidence investigated in the province of Crotone), today poses the technical challenge of processing big SAR stacks with adequate computing resources. Our experiment proves that nowadays, with the GEP it is possible to process a whole time series of 300 Sentinel-1 SAR images at full swath in less than three days, without the necessity for in-house high-performance computers and data-storage capacity (as an approximate figure with which to compare this time performance, the same processing in a standard desktop PC could take over a week or more, depending on its hardware configuration). The latter aspect is frequently overlooked when planning an InSAR investigation, but it should not be. A stack of 300 SLC Sentinel-1 dual-polarization images alone, prior to any pre-processing or InSAR analysis, requires approximately 2.4 terabytes of disk space, not to mention that the input data need to be sourced from a repository, and this step normally requires a certain amount of time given the conditions for data downloading from catalogues (e.g., a maximum of two simultaneous image downloads as currently allowed to users by the Copernicus Open Access Hub). On the other hand, it is reasonable to expect that a more direct connection to the data repository and a faster data retrieval, combined with a greater amount of computing resources made available to run the processing steps, can significantly improve the overall performance with total processing times shorter than the \(\sim\)3 days achieved in this experiment. Other P-SBAS studies carried out over the same and larger regions of interest across Italy [54; 59] report processing time demands of less than one day, though they refer to shorter Sentinel-1 stacks (i.e., \(\sim\)120-180) and a different cloud-computing infrastructure (i.e., AWS). Therefore, a one-to-one quantitative comparison of time demands cannot be made against these studies. Nonetheless, there is no doubt that a processing run in the GEP saves time that can instead be invested by users to tailor the processing parameters, post-process and interpret the output InSAR products to address their scientific and/or technical goals (e.g., creating a hazard map, estimating landslide intensity, modeling the subsidence process).