At EDF, a leading energy company, process data produced in power stations are archived both to comply with legal archiving requirements and to perform various analysis applications. Such data consist of timestamped measurements, retrieved for the most part from process data acquisition systems. After archival, past and current values are used for various applications, including device monitoring, maintenance assistance, decision support, statistics publication, etc. Large amounts of data are generated in these power stations, and aggregated in soft real-time (without operational deadlines) at the plant level by local servers. For this long-term data archiving, EDF relies on data historians (like InfoPlus.21, PI or Wonderware Historian) for years. This is also true for other energy companies worldwide and, in general, industry based on automated processes. In this paper, we aim at answering a simple, yet not so easy, question: how can data historians be placed in the data management landscape, from classical RDBMSs to NoSQL systems? To answer this question, we first give an overview of data historians, then discuss benchmarking these particular systems. Although many benchmarks are defined for conventional database management systems, none of them are appropriate for data historians. To establish a first objective basis for comparison, we therefore propose a simple benchmark inspired by EDF use cases, and give experimental results for data historians and DBMSs.