  Data stores are the foundation on which data science, in all its variations,
is built upon. They provide a queryable interface to structured and
unstructured data. Data science often starts by leveraging these query features
to perform initial data preparation. However, most data stores are designed to
run continuously to service disparate user requests with little or no downtime.
Many HPC architectures process user requests by job queue scheduler and
maintain a shard filesystem to store a jobs persistent data. We deploy a
MongoDB sharded cluster with a run script that is designed to run a data
science workload concurrently. As our test piece, we run data ingest and data
queries to measure the performance with different configurations on the Blue
Waters supper computer.
