Climate change triggers a wide range of hydrometeorological, glaciological, and geophysical processes that span across vast spatiotemporal scales. With the advances in technology and analytics, a multitude of remote sensing (RS), geodetic, and in situ instruments have been developed to effectively monitor and help comprehend Earth’s system, including its climate variability and the recent anomalies associated with global warming. A huge volume of data is generated by recording these observations, resulting in the need for novel methods to handle and interpret such big datasets. Managing this enormous amount of data extends beyond current computer storage considerations; it also encompasses the complexities of processing, modeling, and analyzing. Big datasets present unique characteristics that set them apart from smaller datasets, thereby posing challenges to traditional approaches. Moreover, computational time plays a crucial role, especially in the context of geohazard warning and response systems, which necessitate low latency requirements.