There are several Big Data platforms, architectures and frameworks already out there and more are coming out each day, figuratively speaking! In such an ecosystem it is difficult to truly measure or characterize the performance of a data processing infrastructure using these frameworks. We abstract out the frameworks into three categories - Batch, Query and Streaming. In this paper, we identify characteristics for each kind of framework and present the results of running heterogeneous workloads for batch frameworks such as Hadoop, stream frameworks such as Spark and query frameworks such as Impala on target cloud-based infrastructure. In our experiments, we have seen performance variations given the multi-tenant nature of the infrastructure and have accounted for these temporal conditions by running our experiments at different times.