When there is an absence of or no implementation of measures targeting disaster prevention, natural hazards can be promptly converted into disasters. An essential part of disaster risk management is the geospatial modelling of devastating hazards, where the polygon shapefiles containing the extent, time period and cause of various disastrous events are significant input datasets in the context of early-warning systems. However, equally important are Earth observation satellite data for constructing remote sensing indices, a high-resolution digital elevation model, updated land cover data, socio-economic data like demographics and spatially referenced data, such as wind characteristics, temperature and precipitation. This research work points out the usefulness of Spatial Data Infrastructures (SDIs) in disaster risk reduction through a literature review, focusing on the necessity of data unification and disposal. Initially, the principles and implementations of SDIs are presented, and subsequently, their benefits for achieving the specific targets and priorities of the Sendai Framework for Disaster Risk Reduction 2015-2030 are elaborated. Thereafter, the challenges in SDIs are investigated in order to underline the main drawbacks the stakeholders in emergency management have to come up against, namely a lack of semantic alignment, which induces a time-consuming data search, and malfunctions in the interoperability of the datasets and web services, the non-availability of the data in spite of their existence and a dearth of quality data. Thus, diachronic observations on disasters will not be found, despite these comprising a meaningful dataset in disaster mitigation. Consequently, recommendations for an efficient SDI that is geared towards natural hazards are proposed to the involved participants for the purpose of disaster preparedness. SDIs constitute an ongoing collaborative effort intending to offer valuable operational tools in decision-making under the threat of a devastating event. Notwithstanding the functionality of SDIs, data collection is an intricate task.