  An increasing number of Analytics-as-a-Service solutions has recently seen
the light, in the landscape of cloud-based services. These services allow
flexible composition of compute and storage components, that create powerful
data ingestion and processing pipelines. This work is a first attempt at an
experimental evaluation of analytic application performance executed using a
wide range of storage service configurations. We present an intuitive notion of
data locality, that we use as a proxy to rank different service compositions in
terms of expected performance. Through an empirical analysis, we dissect the
performance achieved by analytic workloads and unveil problems due to the
impedance mismatch that arise in some configurations. Our work paves the way to
a better understanding of modern cloud-based analytic services and their
performance, both for its end-users and their providers.
