To accommodate cybersecurity requirements of sensitive geospatial data, automation can employ cloud-based or on-premise compute resources, using either Windows or Linux OSs. Native support for distributed processing of the most intense processing steps allows many tasks runtime to scale inversely with the number of participating hosts and the performance of the interconnecting network. When processing multiple pipelines in parallel, free hosts can dynamically participate in processing.

Visualization of processing timelines allows targeted optimization of tasks to meet timeline constraints imposed by business needs: