  We present some of the most widely used architectures for Big Data,
\textit{Hadoop} and \textit{Spark}, and develop several implementations
exploiting, the advantages of each. We implement a simplified version of the
primal-dual optimization algorithm, described briefly in this paper, by
choosing the smoothing functions to be $\Vert \cdot \Vert^2$ with a zero center
point. Under the assumption that data is provided as a sparse matrix, we assess
the scalability of the designed systems empirically by running them on sample
tests.
