This paper attempts to fill this gap by presenting the current landscape of data management solutions/database systems used in science and engineering, providing insights into common technologies, benefits and limitations, looking for emerging trends, and identifying potential white spots that need to be addressed. It also tries to find out how these systems benefit, what the limitations are and how they are used and applied in the set of scientific and engineering fields. The present systematic review also covers a wide range of database systems, including relational databases, object-oriented databases, NoSQL databases, and graph databases, by structuring them into subdomains. The paper continues by identifying and comparing the features, the data model and structure, the query language, the scalability, and performance, the suitability for several scientific and engineering applications domains such as data integration, usage for data analytics, data visualization, and simulation. Finally, the paper concludes by providing a series of general considerations regarding the most appropriate database system or systems for the carriedout needs and type of data (such as challenges or open research questions to be considered, especially distributed and security-related concepts), future research and developments in the database systems field in science and engineering.