Maximum Sustainable Throughput (MST) refers to the amount of data that a Data Stream Processing (DSP) system can ingest while keeping stable performance. It has been acknowledged as an accurate metric to evaluate the performance of stream data processing. Yet, existing operators placements continue to focus on latency and throughput, not MST, as main performance objective when deploy- ing stream data applications in the Edge. In this paper, we argue that MST should be used as an optimization objective when placing operators. This is specially important in the Edge, where network bandwidth and data streams are highly dynamic. We demonstrate that through the design and evaluation of a MST-driven operators placement (based on constraint programming) for stream data applications. Through simulations, we show how existing placement strategies that target overall communications reduction often fail to keep up with the rate of data streams. Importantly, the constraint programming-based operators placement is able to sustain up to 5x increased data ingestion compared to baseline strategies.