Big Data Analytics can enable effective data interpretation that leads to high-quality decision making and higher chances of reaching strategic goals. The amount of data produced and processed each day has vastly increased in the recent years, in turn increasing the need of insightful visualization. Even though there is a large collection of visualization techniques, it is still lacking of understanding how to choose visualization techniques. This thesis aims to examine typical and state-of-the-art data visualization techniques to visualize the data. The main goal is to propose a prototype that can recommend effective visualizations based on the specific data and use scenarios.