Internet of Things (IoT) applications like smart cars, smart cities and wearables are becoming widespread and are the future of the Internet. One of the major challenges for IoT applications is efficiently processing, storing and analyzing the continuous stream of incoming data from a large number of connected sensors. We propose a multi-representation based data processing architecture for IoT applications. The data is stored in multiple representations, like rows, columns, graphs which provides support for diverse application demands. A unifying update mechanism based on deterministic scheduling is used to update the data representations, which completely removes the need for data transfer pipelines like ETL (Extract, Transform and Load). The combination of multiple representations, and the deterministic update mechanism, provides the ability to support real-time analytics and caters to IoT applications by minimizing the latency of operations like computing pre-defined aggregates.