In recent years, a number of benchmark suites have been created for the "Big Data" domain and a number of such applications fit the client-server paradigm. A large volume of recent literature in characterizing "Big Data" applications have largely focused on two extremes of the characterization spectrum. On one hand, multiple studies have focused on client-side performance. These involve fine-tuning server- side parameters for an application to get the best client-side performance. On the other extreme, characterization fo- cuses on picking one set of client-side parameters and then reporting the server microarchitectural statistics under those assumptions. While the two ends of the spectrum present in- teresting results, this paper argues that they are not enough and in some cases, undesirable, to drive system-wide archi- tectural decisions in datacenter design. This paper shows that for the purposes of designing an effcient datacenter, detailed microarchitectural characteri- zation of "Big Data" applications is an overkill. It identi-fies four main system-level macro-architectural features and shows that these features are more representative of an ap- plication's system level behavior. To this end, a number of datacenter applications from a variety of benchmark suites are evaluated and classified into these previously identified macro-architectural features. Based on this analysis, the paper further shows that each application class will benefit from a very difierent server configuration leading to a highly effcient, cost-efiective datacenter.