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Continuous streaming of synchro-waveforms, i.e., time-synchronized waveform measurements, can provide a comprehensive record of the status of the power system. The key to unmask the value of such massive data recording is to extract the most informative aspects of the data. In this paper, we develop and test new methods to detect and characterize subcycle events in continuous streaming of synchro-waveforms. The measurements in this study are collected by the authors in a practical test-bed in California. The measurements are made at low-voltage circuits under two different substations, using GridSweep devices with GPS time stamping. Over 40 billion data points were collected during one month. Several practical challenges are addressed, including the computational complexity due to the enormous size of data, the need for realignment between waveform samples and cycles, and the challenges in extracting differential waveforms to reveal the event signatures.