Emerging Internet of Things(IoT) applications are moving from silo and small scale sensor data sharing to composite and large scale ones. With the rapid growth of application scale, IoT applications is going to leverage cloud infrastructure for scalable solutions and real-time services. Thus large volumes of heterogeneous and high frequency sensor data are fed into IoT cloud services for real-time actionable insight, which raises great challenges of performance and adaptability on cloud solutions. In this paper, we propose a streaming based processing infrastructure for high throughput and low latency IoT real-time analytics services. A data adaptive mechanism is also introduced for heterogeneous data stream integration, interpreting and processing with application logics, as well as context stream. We implemented the proposed mechanisms with spark streaming, and deployed real time IoT analytics service in cloud. Experiment results show that the service has a good scalability and high throughput for IoT data analytics.