This was an advanced-level hackathon, ideal for early-career researchers, scientists, and professionals interested in unlocking the full potential of big geospatial data by harnessing the computational power of open HPC systems. Participants left with valuable insights and practical skills to tackle their geospatial challenges at scale. The hackathon was aimed at individuals with master’s or doctoral qualifications, providing them with an opportunity to scale up their own spatio-temporal modeling and data analysis projects. Hackathon participants were expected to have intermediate bash and Python skills, basic ML-python knowledge, and a strong desire to learn command line tools for massive geo data processes. R users were also welcome if they felt comfortable with command line operations in bash and Python environments. Basic concepts of GIS, such as familiarity with rasters/vectors, overlays, buffering, etc., as well as basics of statistics, such as mean, standard deviation, and residuals, along with Python and bash syntax, were assumed as given. Tips and tricks on massive data processing were shared, making a strong foundation in bash and Python essential for grasping nuanced concepts that could boost geo-analysis.