In the recent past, the number of surveillance cameras placed in the public has increased significantly, and an enormous amount of visual data is produced at an alarming rate. Resultantly, there is a demand for a distributed system for video analytics. However, a majority of existing research on video analytics focuses on improving video content management and rely on a traditional client/server framework. In this project, we develop a scalable and flexible architecture called SIAT on top of general-purpose big data technologies for intelligent video big data analytics in the cloud. The proposed architecture acquires video streams from device-independent data-sources utilizing distributed streams and file management systems. High-level abstractions are provided to allow the researcher to develop and deploy video analytics algorithms and services in the cloud under the as-a-service paradigm.