Online monitoring is the task of identifying complex temporal patterns while incrementally processing streams of data-carrying events. Existing state-of-the-art monitors for first-order patterns, which may refer to and quantify over data values, can process streams of modest velocity in real-time: a few thousands events per second. We scale up first-order monitoring to substantially higher velocities by slicing the stream, based on the events’ data values, into substreams that can be monitored independently. Because monitoring is not data parallel in general, slicing can lead to data duplication. To reduce this overhead, we adapt hash-based partitioning techniques from databases to the monitoring setting. We implement these techniques in an automatic data slicer based on Apache Flink and empirically evaluate its performance using two tools—MonPoly and DejaVu—to monitor the substreams. Our evaluation demonstrates a substantial scalability improvement for both tools.