Optimizing Hadoop executions has attracted a lot of research contributions in particular in the domain of self-adaptive software systems. However, these research efforts are often hindered by the complexity of Hadoop operation and the difficulty to reproduce experimental evaluations that makes it hard to compare different approaches to one another. To address this limitation, we propose a research acceleration platform for rapid prototyping and evaluation of self-adaptive behavior in Hadoop clusters. Essentially, it provides automated approach to provision reproducible Hadoop environments and execute acknowledged benchmarks. It is based on the state-of-the-art container technology that supports both distributed configurations as well as standalone single-host setups. We demonstrate the approach on a complete implementation of a concrete Hadoop self-adaptive case study. The artifact is available at: https://github.com/Spirals-Team/hadoop-benchmark/raw/SEAMS17/artifact.zip