The size of data sets is increasing to the point where we cannot store it in a single machine. This is a problem for standard algorithms which make this assumption. Distributed algorithms shine in such cases because they do not require a single machine to store the entire input. In this dissertation, we explore the power and limitations of several distributed computing models by developing fast algorithms and showing unconditional lower bounds.