This thesis explores the application of graph databases in the context of regular path queries. First, the basic definitions related to graph databases are given, as well as an overview of different models of these databases, followed by simulation between different database models. Furthermore, the thesis focuses on algorithms for calculating regular path queries and provides an overview of the main important types of paths (walk, trail, simple, and acyclic), as well as cases when all shortest, any, or any shortest paths are selected. The complexities of the algorithms are derived, and a result of the NP-completeness of the language ANY SIMPLE PATH is proven. The algorithms were implemented in the Python programming language and the code is publicly available at https://github.com/mdujic/diplomski. As part of the implementation, Glushkov’s algorithm was applied, and the process of constructing a nondeterministic finite automaton with a unique final state was described. Additionally, an implementation of a graph database using a Trie data structure is presented. In the experiments, tests were performed on artificial examples with an exponential number of cases, as well as on real-world examples. These tests serve to verify the effectiveness and scalability of the developed algorithms. In conclusion, this thesis provides an overview of the application of graph databases and algorithms for calculating regular path queries. Different database models, implementations, and conducted experiments that demonstrated the success of the developed algorithms were explored. This work contributes to the understanding and development of graph databases in the context of regular path queries