  Collecting, analyzing and gaining insight from large volumes of data is now
the norm in an ever increasing number of industries. Data analytics techniques,
such as machine learning, are powerful tools used to analyze these large
volumes of data. Synthetic data sets are routinely relied upon to train and
develop such data analytics methods for several reasons: to generate larger
data sets than are available, to generate diverse data sets, to preserve
anonymity in data sets with sensitive information, etc. Processing,
transmitting and storing data is a key issue faced when handling large data
sets. This paper presents an "On the fly" framework for generating big
synthetic data sets, suitable for these data analytics methods, that is both
computationally efficient and applicable to a diverse set of problems. An
example application of the proposed framework is presented along with a
mathematical analysis of its computational efficiency, demonstrating its
effectiveness.
