  Streaming Dynamic Mode Decomposition (sDMD) (Hemati et al., Phys. Fluids
26(2014)) is a low-storage version of Dynamic Mode Decomposition (DMD) (Schmid,
J. Fluid Mech. 656 (2010)), a data-driven method to extract spatio-temporal
flow patterns. Streaming DMD avoids storing the entire data sequence in memory
by approximating the dynamic modes through incremental updates with new
available data. In this paper, we use sDMD to identify and extract dominant
spatio-temporal structures of different turbulent flows, requiring the analysis
of large datasets. First, the efficiency and accuracy of sDMD are compared to
the classical DMD, using a publicly available test dataset that consists of
velocity field snapshots obtained by direct numerical simulation of a wake flow
behind a cylinder. Streaming DMD not only reliably reproduces the most
important dynamical features of the flow; our calculations also highlight its
advantage in terms of the required computational resources. We subsequently use
sDMD to analyse three different turbulent flows that all show some degree of
large-scale coherence: rapidly rotating Rayleigh--B\'enard convection,
horizontal convection and the asymptotic suction boundary layer. Structures of
different frequencies and spatial extent can be clearly separated, and the
prominent features of the dynamics are captured with just a few dynamic modes.
In summary, we demonstrate that sDMD is a powerful tool for the identification
of spatio-temporal structures in a wide range of turbulent flows.
