Data visualization and analytics are nowadays one of the corner-stones of Data Science, turning the abundance of Big Data being produced through modern systems into actionable knowledge. Indeed, the Big Data era has realized the availability of voluminous datasets that are dynamic, noisy and heterogeneous in nature. Transforming a data-curious user into someone who can access and analyze that data is even more burdensome now for a great number of users with little or no support and expertise on the data processing part. Thus, the area of data visualization and analysis has gained great attention recently, calling for joint action from different research areas and communities such as information visualization, data management and mining, human-computer interaction, and computer graphics. This article presents the limitations of traditional visualization systems in the Big Data era. Additionally, it discusses the major prerequisites and challenges that should be addressed by modern visualization systems. Finally, the state-of-the-art methods that have been developed in the context of the Big Data visualization and analytics are presented, considering methods from the Data Management and Mining, Information Visualization and Human-Computer Interaction communities