In recent years, the amount of intelligent CCTV cameras installed in public places for surveillance has increased enormously and as a result, a large amount of video data is produced every moment. Due to this situation, there is an increasing request for distributed processing of large-scale video data. In the intelligent video analytics platform, a submitted unstructured video undergoes through several multidisciplinary algorithms with the aim to extract the insight and make them searchable and understandable knowledge for both human and machine. Video analytics has applications ranging from surveillance to video content management. In this context, various industrial and scholarly solutions exist. However, most of the existing solutions rely on a traditional client/server framework to perform face and object recognition while lacks the support for more complex application scenarios. Furthermore, these frameworks are rarely handled in a scalable manner using distributed computing. Besides, existing works do not provide any support for low-level distributed video processing APIs. They also failed to address a complete service-oriented ecosystem to meet the growing demands of consumers, researchers, and developers. In order to overcome these issues, in this paper, we propose a distributed video analytics framework for intelligent video surveillance known as SIAT. The proposed framework is able to process both the real-time video streams and batch video analytics. Furthermore, we introduce a distributed video processing library on top of Spark using the map function. SIAT exploits state-of-the-art distributed computing technologies with the aim to ensure scalability, effectiveness, and fault-tolerance. Lastly, we implant and evaluate our proposed framework with the goal to authenticate our claims.