The frequent occurrence of urban rainstorm waterlogging is a severe challenge for China’s rapid urbanization, which demands prompt countermeasures. However, the fact is that the meteorological data, disaster data, and socio-economic data about rainstorm waterlogging have not been fused effectively at present, which makes it difficult to make emergency response plans in a quick and effective manner and thus hinders the emergency management. The emergency management of urban rainstorm waterlogging is a typical problem of multi-disciplines and big data, involved with meteorology, geography, information science, city planning, economics, management science and other disciplines. The great mass of related data possess rich resources, such as meteorological data, urban waterlogging data, socio-economic data, etc. As these data come from such different departments as water conservancy, meteorology, urban management, operators and Internet, with incompatible temporal and spatial scales as well as non-uniform format standards, thus bringing a huge obstacle to emergency decisions. Therefore, how to achieve the integration of the big data of disasters has become an urgent key issue. In the context of big data, urban rainstorm waterlogging ontology should be constructed in the first place, and then multi-source heterogeneous data should be effectively fused, big data dimension reduced and socio-economic losses assessed. Based on that, an emergency decision can be quickly formed with the help of expert decisions. This is concerned with such contents as large data ontology technology, data fusion methods, data dimension reduction and disaster identification, socio-economic loss assessment of disasters as well as the formation and evaluation of emergency responses. This paper, proceeding from the five parts of the big data fusion of urban rainstorm waterlogging, reviews such theoretical and practical development as ontology technology, data fusion method, data dimension reduction, disaster identification, socio-economic loss assessment of disasters, as well as the drawing up and assessment method of emergency response plans. Generally speaking, China is in urgent need of multi-disciplinary integration to collectively tackle critical problems. Future studies can be carried out in the following ways: First, design big data standard system of urban rainstorm waterlogging, so as to realize the semantic annotation of city waterlogging. Second, conduct in-depth researches on such three aspects as grid non-spatial attribute data, unified spatial coordinate system, together with the general algorithm of soft and hard data fusion, in order to solve the problems of the consistency and association between soft data and hard data. Third, extract complex changes in urban rainstorm waterlogging from multifarious data, so as to realize the dimension reduction of high dimensional data and the recognition of urban rainstorm waterlogging. Fourth, construct the risk database of urban rainstorm waterlogging disasters and assess the economic impacts of typical urban waterlogging on related regions, industries and departments, in order to provide data and scenario support for the emergency management of urban rainstorm waterlogging. Fifth, based on the fusion platform of meteorological data, urban waterlogging data and socio-economic data, by means of case-based reasoning (CBR), rule-based reasoning (RBR) and geographic information system (GIS), generate emergency response plans, and then evaluate and adjust them according to the scenario changes. Finally, what is pointed out is that the research should be carried out on the emergency management of urban waterlogging in the context of big data fusion, which can enrich the theories and methods of the emergency management of urban rainstorm waterlogging and set a typical example for the innovative exploration of the paradigm changes in big-data-driven management decisions, and for the multi-disciplinary development of management science, economics, meteorology and information science.