  In the context of big data analysis, the divide-and-conquer methodology
refers to a multiple-step process: first splitting a data set into several
smaller ones; then analyzing each set separately; finally combining results
from each analysis together. This approach is effective in handling large data
sets that are unsuitable to be analyzed entirely by a single computer due to
limits either from memory storage or computational time. The combined results
will provide a statistical inference which is similar to the one from analyzing
the entire data set. This article reviews some recently developments of
divide-and-conquer methods in a variety of settings, including combining based
on parametric, semiparametric and nonparametric models, online sequential
updating methods, among others. Theoretical development on the efficiency of
the divide-and-conquer methods is also discussed.
