Background and Objective: With the accessibility of good transport system and communication system the gap between places and people have considerably reduced. Millions of people are moving towards big cities form near and far places of rural areas. So, it becomes very difficult to manage the limited number of recourse in cities with high demand. Some of the limited recourses are electricity, water, drainage and transportation. This condition of scarcity of limited resources gives rise to need of smart city technologies. In order to manage these limited resources more effectively we need relevant data to manage these resources and take efficient decisions. The data needed usually is of high velocity nature and are form different sensors across the city. The data produced from these sensors if stored in a data base will not only consume high amount of memory but also becomes useless if it is not used in real time applications. The aim of this paper is to compare such big data architectures that focus on the ways to collect, process the data and enable fast decision making in a smart city context. Materials and Methods: The contents of the paper includes the literature review of smart sensing using optical sensors to give users a basic understanding of how data in a smart city is collected and then monitored in real time. The paper also provides review of two architectures (BASIS and CIDAP) focusing on its functionalities and usage criteria. Conclusions: The paper presents a comparative analysis of BASIS and CIDAP architectures highlighting its strengths, limitations and applicability. © 2016, International Journal of Pharmacy and Technology. All rights reserved.