The COVID-19 pandemic has emerged as one of the most crucial health emergencies in the last decade where almost all entities of a nation’s ecosystem like inhabitants, businesses, governments, economies, and environment are impacted. The large volumes of epidemiological, clinical, personal, and environmental data generated during any pandemic can provide useful insights about the underlying causes, symptoms, relations, and correlations, which if analyzed can assist in mitigating the impact to a great extent. The cheap and easy connectivity and communication provided by the social media platforms (SMP) have established them as one of the most preferred mediums of communications among the masses. The data generated by these platforms can be analyzed in context of the ongoing COVID-19 crisis to provide critical information and insights related to the ground level realities like spread and severity of infection, the state of implementation of control measures, the mental state of individuals, and their needs. The tweets and comments of the users can provide information about the current situation and intensity of the problems in the affected regions. With the help of techniques like sentiment analysis and web mining, we can identify the emergent requirements and needs (like food, shelter, medicine, medical emergencies, security, etc.) of the population in the COVID-19-affected areas. With this chapter we aim to identify several use cases where the big medical data from the patients, epidemiological data, social media data, and environment-related data can be used to identify patterns, causes, and other growing factors of the COVID-19 pandemic with a goal to mitigate the damages and contain further spread of the disease. The chapter also discusses the impact of a preferred mitigation measure of nationwide lockdown on the number of new novel coronavirus-positive patients as well as the impact on the environment by analyzing the available data. Since the tourism industry is now of the worst hit businesses, we also discussed the impact of COVID-19 on tourism industry. Furthermore, we identify the challenges associated with handling the massive amount of data generated during such pandemics. Finally, the future avenues of using big data for effectively devising predictive mechanisms and techniques to contain such pandemics in the initial stages are discussed. The chapter also discusses the importance of edge/fog technologies and IoT to identify possible use cases and where immediate point of contact actions is needed to mitigate the situations. Since edge computing facilitates calculations near the origin of data, it is imperative to understand the potential use cases in times of COVID-19-like pandemics.