  The general increase in data size and data sharing motivates the adoption of
Big Data strategies in several scientific disciplines. However, while several
options are available, no particular guidelines exist for selecting a Big Data
engine. In this paper, we compare the runtime performance of two popular Big
Data engines with Python APIs, Apache Spark, and Dask, in processing
neuroimaging pipelines. Our experiments use three synthetic \HL{neuroimaging}
applications to process the \SI{606}{\gibi\byte} BigBrain image and an actual
pipeline to process data from thousands of anatomical images. We benchmark
these applications on a dedicated HPC cluster running the Lustre file system
while using varying combinations of the number of nodes, file size, and task
duration. Our results show that although there are slight differences between
Dask and Spark, the performance of the engines is comparable for data-intensive
applications. However, Spark requires more memory than Dask, which can lead to
slower runtime depending on configuration and infrastructure. In general, the
limiting factor was the data transfer time. While both engines are suitable for
neuroimaging, more efforts need to be put to reduce the data transfer time and
the memory footprint of applications.
