  The stellar occultation technique provides competitive accuracy in
determining the sizes, shapes, astrometry, etc., of the occulting body,
comparable to in-situ observations by spacecraft. With the increase in the
number of known Solar System objects expected from the LSST, the highly precise
astrometric catalogues, such as Gaia, and the improvement of ephemerides,
occultations observations will become more common with a higher number of
chords in each observation. In the context of the Big Data era, we developed
SORA, an open-source python library to reduce and analyse stellar occultation
data efficiently. It includes routines from predicting such events up to the
determination of Solar System bodies' sizes, shapes, and positions.
