The number of domains affected by the big data phenomenon is constantly increasing, both in science and indus- try, with high-throughput DNA sequencers being among the most massive data producers. Building analysis frameworks that can keep up with such a high production rate, however, is only part of the problem: current challenges include dealing with articulated data repositories where objects are connected by multiple re- lationships, managing complex processing pipelines where each step depends on a large number of configuration parameters and ensuring reproducibility, error control and usability by non- technical staff. Here we describe an automated infrastructure built to address the above issues in the context of the analysis of the data produced by the CRS4 next-generation sequencing facility. The system integrates open source tools, either written by us or publicly available, into a framework that can handle the whole data transformation process, from raw sequencer output to primary analysis results.