## 1 Introduction  
With the advent of the European Commission's Copernicus two-satellite Sentinel-1 constellation, operated by the European Space Agency (ESA), a massive volume of high-quality C-band Synthetic Aperture Radar (SAR) observations with moderate spatial (2-14 m) and temporal resolution (6-12 days) has become freely available [1]. With a 250-km-wide cross-track coverage in the default Interferometric Wide Swath (IWS) mode, Sentinel-1 provides a unique and powerful dataset that has the potential to be used for monitoring surface deformation at spatial scales ranging from a few meters to tens of kilometers. Interferometric Synthetic Aperture Radar (InSAR) is a particularly suitable technique for measuring deformation induced by various geophysical phenomena, including theoseismic [2; 3; 4], postseismic [5; 6; 7; 8] and interseismic phases of the earthquake deformation cycle [9; 10; 11; 12], volcanic movements [13; 14; 15], terrain deformation due to geothermal activities [16; 17; 18] and slow-moving landslides [19; 20]. Since the launch of Sentinel-1B in 2016, data are acquired globally with a typical revisit period of 12 days, and every 6 days in Europe [21]. The relatively short revisit time (compared to 35-days of previous ESA SAR satellites) is a significant advance because interferograms spanning a short interval usually maintain better coherence and allow a more accurate estimate of rapid deformation. The short revisit time also leads to a greater number of acquisitions, which is useful for statistical reduction of the noise contribution (e.g., due to atmospheric phase delay) in InSAR time series analyses [22]. The data is particularly important for global monitoring of tectonic and volcanic activities [23]. Over the last five years, numerous earthquakes and volcanic eruptions have already been imaged by Sentinel-1 interferograms, aiding rapid response and resulting in a greater scientific understanding of the events and their geophysical properties [8; 24; 25; 26]. Despite the opportunities provided by the increasing availability of Sentinel-1 SAR data, it is still difficult to take full advantage of these resources, particularly for large-scale applications. Indeed, the full exploitation of the large amount of SAR data provided by the Sentinel-1 system, with its short revisit time and global-scale coverage (data are produced in the rate of \(\sim\)13 TB/day), requires specific data preprocessing approaches (e.g., downsampling the SAR data--multilooking) or Big Data processing algorithms involving computer cluster facilities. The computing and storage demands require specific computing platforms [27]. Various research groups base their systematic processing approaches on open-source software such as SNAP [28], ISCE [29] or GMTSAR [30] for generation of differential interferograms and GMTSAR, STAMPS [31] or other tools as their time series processor. Other institutions prefer commercial software equivalents such as GAMMA [32; 33], ENVI SARS Cape [34] or SARPROZ [35], to list the currently most recognized available tools. As the deployment of an InSAR processing system is strongly connected to the storage and computing facilities required, there is currently a lack of recognized deployable system solutions, although with exceptions such as IT4S1 [36], partly based on the metadata database approach described here. In recent years, there have been remarkable developments in promoting the idea of using cloud computing technology to address the storage and processing of remote sensing data. Significant efforts are underway to facilitate access to high-performance computing (HPC) resources and processing of very large Earth Observation (EO) datasets by such institutions as the National Aeronautics and Space Administration (NASA), the United States Geological Survey (USGS), company-based infrastructures such as the Google Earth Engine (GEE), the Amazon Web Services (AWS), or science oriented HPC platforms such as the Earth Observation Data Center (EODC) [37].