Stream Reasoning is a well established field not only in the Semantic Web, but is also adapted in the knowledge representation and reasoning and AI community in general. In the Semantic Web area, there have been valuable efforts in building data generators and benchmarks, however they are not well suited for evaluating more expressive stream reasoning approaches, since the focus is on a graph-based data model and more limited reasoning features, such as query answering. This paper aims at filling the gap, so the different communities can compare, discuss, and benchmark the various approaches for stream reasoning based on a common playground. We will present the stream reasoning playground that targets streaming reasoning as the first-class modelling and processing feature. Our playground includes an easy-to-extend platform for data stream generation with pluggable data formatters, whereby different data stream sources, and modelling problems for two interesting application scenarios, i.e., intelligent traffic management and vehicle stream data analytics, are provided. Furthermore, we present a more generic scenario for time-series data, where a workflow for streaming time-series data from various datasets is facilitated by using mapping functions. To illustrate a first application of the playground, we report on the usage experience of well-known stream reasoner developers in the “model and solve” Hackathon event of the annual Stream Reasoning workshop.