While Next-Generation Sequencing (NGS) can now be considered an established analysis technology for research applications across the life sciences, the analysis workflows still require substantial bioinformatics expertise. Typical challenges include the appropriate selection of analytical software tools, the speedup of the overall procedure using HPC parallelization and acceleration technology, the development of automation strategies, data storage solutions and finally the development of methods for full exploitation of the analysis results across multiple experimental conditions. Recently, NGS has begun to expand into clinical environments, where it facilitates diagnostics enabling personalized therapeutic approaches, but is also accompanied by new technological, legal and ethical challenges. There are probably as many overall concepts for the analysis of the data as there are academic research institutions. Among these concepts are, for instance, complex IT architectures developed in-house, ready-to-use technologies installed on-site as well as comprehensive Everything as a Service (XaaS) solutions. In this mini-review, we summarize the key points to consider in the setup of the analysis architectures, mostly for scientific rather than diagnostic purposes, and provide an overview of the current state of the art and challenges of the field.