The paper proposes a system that analyzes Bike Sharing Systems (BSS) in five cities of San Francisco Bay Area using Big Data technologies and investigates the current conditions and future trends. This research provides analysis on the number of bikes and docks available at each station, bikes trips and station trips, bike user type, station demand, bikes revenue and trip-weather relation, all in real time. Moreover, the system also predicts the demand for bikes and trips at particular station at particular time by using four different regression algorithms. Hence, this study is relevant from both an academic and a practitioners perspective.