The Universe holds several avenues for the (catastrophic) end of stars. These include their gravitational collapse to a Neutron star, resulting in a so-called core-collapse Supernova, stars being swallowed by the central Black Hole of a galaxy, as well as Kilonova, the result of two merging Neutron stars recently detected for the first through the electromagnetic follow-up of a Gravitational wave event. The diversity of energetic and explosive events serves as a laboratory for fundamental physics that is explored through increasingly powerful observational facilities. With the start of the Zwicky Transient Facility (ZTF), the detection rate of time-variable phenomena in the Universe will increase by a factor 10 compared to existing surveys, far beyond what can be manually examined by astronomers. This PhD project focuses on developing new data management and machine learning approaches that will allow the scalable analysis of ZTF data through the implementation of flexible/scalable data infrastructure for classifying new transients. As the computing resources needed for this kind of computation will vary, there is also the need to manage them in an elastic manner thus leading to new monitoring and resource management strategies.