Convergent beam electron diffraction (CBED) patterns acquired with scanning transmission electron microscopy (STEM) contain abundant specimen information that is inaccessible in conventional STEM with annular detectors. However, use of this “4D-STEM” technique has been hindered by two primary challenges: (1) Pixel array detectors are much slower than conventional annular detectors, which limits the specimen field-of-view of 4D-STEM experiments, and (2) Data sets from 4D-STEM experiments are large and unwieldy, which makes interpretation of results difficult. To address the first challenge of detector speed, we have customized a high-speed direct detection camera for synchronized 4D-STEM acquisition at several kilohertz, which is faster than other currently-available pixel area detectors. This increased speed enables collection of larger specimen areas without unacceptable specimen drift. Then, to address the second challenge, we have developed an open-source Python program for robust analysis of large 4D-STEM datasets. As a proof-of-concept, this combination of hardware and software have been used to analyze the structure of several materials specimens in an aberration-corrected electron microscope.