  Parametric data-driven modeling is relevant for many applications in which
the model depends on parameters that can potentially vary in both space and
time. In this paper, we present a method to obtain a global parametric model
based on snapshots of the parameter space. The parameter snapshots are
interpolated using the classical univariate Loewner framework and the global
bivariate transfer function is extracted using a linear fractional
transformation (LFT). Rank bounds for the minimal order of the global
realization are also derived. The results are supported by various numerical
examples.
