  In order to cope with the relentless data tsunami in $5G$ wireless networks,
current approaches such as acquiring new spectrum, deploying more base stations
(BSs) and increasing nodes in mobile packet core networks are becoming
ineffective in terms of scalability, cost and flexibility. In this regard,
context-aware $5$G networks with edge/cloud computing and exploitation of
\emph{big data} analytics can yield significant gains to mobile operators. In
this article, proactive content caching in $5$G wireless networks is
investigated in which a big data-enabled architecture is proposed. In this
practical architecture, vast amount of data is harnessed for content popularity
estimation and strategic contents are cached at the BSs to achieve higher
users' satisfaction and backhaul offloading. To validate the proposed solution,
we consider a real-world case study where several hours of mobile data traffic
is collected from a major telecom operator in Turkey and a big data-enabled
analysis is carried out leveraging tools from machine learning. Based on the
available information and storage capacity, numerical studies show that several
gains are achieved both in terms of users' satisfaction and backhaul
offloading. For example, in the case of $16$ BSs with $30\%$ of content ratings
and $13$ Gbyte of storage size ($78\%$ of total library size), proactive
caching yields $100\%$ of users' satisfaction and offloads $98\%$ of the
backhaul.
