Research in data science field has pointed out analytical potentials contained in big data on numerous occasions. The new paradigms in data storage and processing emerged with the goal of handling big data, but also pushing traditional, already present systems out of focus and creating a gap between the old and the new. That was also the case with traditional data warehouses and emergence of NoSQL data stores, whose integration has shown to be quite problematic due to NoSQL and big data features. Latest research has been trying to overcome this gap in various ways, mostly by system integration on various levels or introducing completely different new ones, but due to the data warehouse's particular nature and strict process of data modelling and acquisition, this problem must be approached on a conceptual level, capturing the essence of the domain which is to be analysed. The lack of schema in NoSQL databases makes them less comprehensible for integration and analysis, which motivated the idea of employing semantics to enrich NoSQL database contents, making them more suitable for integration. This paper analyses ways in which semantics could be applied as an asset in bringing together data warehouses, NoSQL databases, and big data.