With the development of urbanization, the rapid growth of urban population density has brought a raise in different types of services and higher demand for up-to-date infrastructures. In order to provide high-quality services and advanced infrastructure to citizens, it is important to understand what their requirements and demands are. The data scattered throughout every corner of the city provides effective information for smart city initiatives. This data comes from sensors, smart mobile phones, and social media, which reflect the big data era. It is essential to investigate big data effects in the smart city due to the increase in data resources and the number of residents. This dissertation undertakes a systematic literature review of 27 academic journals and conference papers which span from 2013 to 2017 in order to study the role of big data in smart city development. It aims to provide a systematic synthesis of the literature to reveal how big data technology supports the evolution of a city to become smart, the benefits of big data applications in the smart city, and the potential negative aspects of big data currently facing the smart city. This dissertation explains how data is collected in an urban environment. It then illustrates two computing infrastructures: cloud- computing and fog computing; three analytical methods: descriptive analytics, predictive analytics, and prescriptive analytics; and two data processing platforms: Hadoop and Spark. Several big data applications are also presented: smart transportation, smart energy, smart security, smart environment, smart healthcare, and smart education. The challenges of big data in the smart city are discussed by focusing on the inappropriate use on big data, big data’s defects, and the growing demand for resources.