Visual Analytics plays an important role in discovering hidden information from massive, heterogenous and streaming data. Visual analytics using visual representations and interactive techniques that are combined with statistical and machine learning methods for analysis process. Big data visual analytics faces many challenges related to technological issues and human cognition. This paper’s aim is to focus on the challenges of big data visual analytics and how the recent platforms including Knime, SAS visual analytics, Arcadia enterprise and TensorFlow could deal with these challenges. It also provides comparison between these platforms. The results show that these platforms can overcome most of the general challenges. TensorFlow is a promising platform for handling challenges related to interaction and user interfaces. Other specific challenges like uncertainty and human cognitive bottlenecks still need more efforts.