  The modern era is characterised as an era of information or Big Data. This
has motivated a huge literature on new methods for extracting information and
insights from these data. A natural question is how these approaches differ
from those that were available prior to the advent of Big Data. We present a
review of published studies that present Bayesian statistical approaches
specifically for Big Data and discuss the reported and perceived benefits of
these approaches. We conclude by addressing the question of whether focusing
only on improving computational algorithms and infrastructure will be enough to
face the challenges of Big Data.
