  We formulate a low-storage method for performing dynamic mode decomposition
that can be updated inexpensively as new data become available; this
formulation allows dynamical information to be extracted from large datasets
and data streams. We present two algorithms: the first is mathematically
equivalent to a standard "batch-processed" formulation; the second introduces a
compression step that maintains computational efficiency, while enhancing the
ability to isolate pertinent dynamical information from noisy measurements.
Both algorithms reliably capture dominant fluid dynamic behaviors, as
demonstrated on cylinder wake data collected from both direct numerical
simulations and particle image velocimetry experiments
