Modern analytics services require the analysis of large quantities of data derived from disparate geo-distributed sources. Further, the analytics requirements can be complex, with many applications requiring a combination of both real-time and historical analysis, resulting in complex tradeoffs between cost, performance, and information quality. While the traditional approach to analytics processing is to send all the data to a dedicated centralized location, an alternative approach would be to push all computing to the edge for in-situ processing. We argue that neither approach is optimal for modern analytics requirements. Instead, we examine complex tradeoffs driven by a large number of factors such as application, data, and resource characteristics. We present an empirical study using PlanetLab experiments with beacon data from Akamai's download analytics service. We explore key tradeoffs and their implications for the design of next-generation scalable wide-area analytics.