The analysis of biomolecular computer simulations has become a challenge because the amount of output data is now routinely in the terabyte range. We evaluated if this challenge can be met by a parallel map-reduce approach with the Dask parallel computing library for task-graph based computing coupled with our MDAnalysis Python library for the analysis of molecular dynamics (MD) simulations. We performed a representative performance evaluation, taking into account the highly heterogeneous computing environment that researchers typically work in together with the diversity of existing file formats for MD trajectory data. We found that the underlying storage system (solid state drives, parallel file systems, or simple spinning platter disks) can be a deciding performance factor that leads to data ingestion becoming the primary bottleneck in the analysis work flow. However, the choice of the data file format can mitigate the effect of the storage system; in particular, the commonly used Gromacs XTC trajectory format, which is highly compressed, can exhibit strong scaling close to ideal due to trading a decrease in global storage access load against an increase in local per-core CPU-intensive decompression. Scaling was tested on a single node and multiple nodes on national and local supercomputing resources as well as typical workstations. Although very good strong scaling could be achieved for single nodes, good scaling across multiple nodes was hindered by the persistent occurrence of “stragglers”, tasks that take much longer than all other tasks, and whose ultimate cause could not be completely ascertained. In summary, we show that, due to the focus on high interoperability in the scientific Python eco system, it is straightforward to implement map-reduce with Dask in MDAnalysis and provide an in-depth analysis of the considerations to obtain good parallel performance on HPC resources.