Heterogeneous architectures that combine multi-core CPUs with many-core GPUs have the potential to improve the performance of data-intensive stream processing applications. Yet, a stream processing engine must execute streaming SQL queries with sufficient data-parallelism to fully utilise the available heterogeneous processors, and decide how to use each processor in the most effective way. Addressing these challenges, we demonstrate SABER, a hybrid high-performance relational stream processing engine for CPUs and GPUs. SABER executes window-based streaming SQL queries in a data-parallel fashion and employs an adaptive scheduling strategy to balance the load on the different types of processors. To hide data movement costs, SABER pipelines the transfer of stream data between CPU and GPU memory. In this paper, we review the design principles of SABER in terms of its hybrid stream processing model and its architecture for query execution. We also present a web front-end that monitors processing throughput.