ARD in a format amenable to the development of ML and DL applications would yield unprecedented opportunities to exploit under-utilised satellite data to develop solutions to global challenges such as flood risk mapping [2], water resource management [3], deforestation [4] and glacial lake mapping [5]. Collocated Sentinel-1 and Sentinel-2 data are also used in applications such as land cover mapping [6], crop type classification [7], cloud removal [8], and soil moisture mapping [9]. A number of analysis-ready datasets have been published in recent years. For Sentinel-1, datasets such as OpenSARShip for ship detection [10] and OpenSAR Urban for urban area classification [11] have been made available. Similarly, for Sentinel-2, the Uganda dataset for environmental monitoring [12], the Eurosat dataset for land use and land cover classification [13] and the TimeSen2 Crop dataset for crop classification [14] have been published. Only a small number of collocated Sentinel-1 and Sentinel-2 analysis-ready datasets are available. For example, the SEN1-2 dataset, consisting of 282,384 pairs of vertically polarised (VV) Sentinel-1 and RGB Sentinel-2 patches, was published for the fusion of Synthetic Aperture Radar (SAR) and optical data sources using deep learning [15]. Subsequently, the SEN12 MS dataset, with 180,662 triplets of dual polarised Sentinel-1, multi-spectral Sentinel-2 and MODIS land cover maps, was released [16]. The So2 Sat LCZ42 dataset, consisting of Sentinel-1 and Sentinel-2 patches with local climate zone (LCZ) labels, was published for LCZ classification [17]. However, the pre-defined location and temporal span of these datasets restrict their use. Earth Observation Data Cubes (EODCs) are an emerging trend in terms of providing organised data as a multi-dimensional array in a time-ordered manner. EODC was conceptualised at a nation-wide scale by the Government of Australia through the Landsat archive data [18; 19; 20], the aims being to address the challenges of volume, variety and velocity in handling Big Data for EO applications. Recently, the Committee on Earth Observation Satellites (CEOS) has championed the Open Data Cube (ODC) initiative in an effort to mitigate the difficulties in generating and accessing ARD. A number of data cubes for multiple territories have become available using ODC technology, e.g., Switzerland [21; 22], Columbia [23], Taiwan [24], Australia [25] and Africa [26], providing a combination of SAR and optical data. A recent effort has expanded the scope of ARD to include SAR data with interferometric coherence along with normalised radar backscatter and dual-polarization decomposition for Australia [27]. Currently, nine open data cubes are operational, 14 in development and 32 under review [28], not including those that, as yet, have not been reported. Alongside the ODC initiative, platforms such as FORCE (Framework for Operational Radiometric Correction for Environmental monitoring) implement combined processing pipelines for optical data (Sentinel-2 and Landsat) with the capability to generate cubes [29]. Moreover, EarthServer provides access to spatio-temporal EO data using the Rasdaman array-database system [30] while the Google Earth engine provides access to high-performance computing resources for geospatial data [31]. However, these cloud-based platforms restrict analyses only to their platform and thus limit the development and scope of local solutions [32]. The establishment and ease of accessibility for a spectrum of data cubes for different geographies and applications is a significant first step; however, the configuration, management and operation of a data cube remains cumbersome, hindering the advancement of the state-of-the-art in Earth sciences and the development of more functional applications. The recent proof-of-concept Data Cube on Demand (DCoD) methodology--tested in two sites in Bolivia and the Democratic Republic of Congo (DRC) in the field of environmental monitoring [33]--proposed the use of virtualisation technology to permit rapid operationalisation.