ZERoTuNE introduces a novel cost model for parallel and distributed stream processing that can be used to effectively set initial parallelism degrees of streaming queries. Unlike existing models, which rely majorly on online learning statistics that are non-transferable, context-specific, and require extensive training, ZERoTuNE proposes data-efficient zero-shot learning techniques that enable very accurate cost predictions without having observed any query deployment.