In this paper we document our approach to overcoming service discovery and configuration of Apache Hadoop and Spark frameworks with dynamic resource allocations in a batch oriented Advanced Research Computing (ARC) High Performance Computing (HPC) environment. ARC efforts have produced a wide variety of HPC architectures. A common HPC architectural pattern is multi-node compute clusters with low-latency, high-performance interconnect fabrics and shared central storage. This pattern enables processing of workloads with high data co-dependency, frequently solved with message passing interface (MPI) programming models, and then executed as batch jobs. Unfortunately, many HPC programming paradigms are not well suited to big data workloads which are often easily separable. Our approach lowers barriers of entry to HPC environments by enabling end users to utilize Apache Hadoop and Spark frameworks that support big data oriented programming paradigms appropriate for separable workloads in batch oriented HPC environments.