With the rapid growth of collected data and the variety of its content, the need for efficient integration at a Big Data level becomes crucial. Semantic technologies, as a means of integration and coordination of heterogeneous systems, may help big data to manage terminology and relationships to link various data from different data sources. However, and due to the difficulty of integration and analytics of some datasets with high-precision, automated processes cannot reach a high level of accuracy without the human cognitive ability. Crowdsourcing platforms have the potential to integrate (entity matching, entity resolution) and analyze (sentiment analysis, image recognition) heterogeneous data sources when in some cases these integration tasks may prove to be problematic for computers. In this survey, we explore and compare empirical research studies that rely on merging semantic and crowdsourcing technologies. And, in the light of this comparison, we propose a high-level integration workflow, which shows how merging these technologies can enhance the big data integration process and tackle the data analysis challenges.