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

翻译：数据可视化与分析如今是数据科学的基石之一，将现代系统产生的大量大数据转化为可操作的知识。事实上，大数据时代实现了大规模数据集的可用性，这些数据集本质上是动态、嘈杂且异质的。对于大量在数据处理方面缺乏支持或专业知识的数据好奇用户而言，将他们转化为能够访问和分析这些数据的人，现在变得更加困难。因此，数据可视化与分析领域近年来引起了广泛关注，呼吁来自不同研究领域和社群（如信息可视化、数据管理与挖掘、人机交互和计算机图形学）的联合行动。本文阐述了传统可视化系统在大数据时代的局限性。此外，它讨论了现代可视化系统应解决的主要前提和挑战。最后，介绍了在大数据可视化与分析领域已开发的最新方法，考虑了来自数据管理与挖掘、信息可视化和人机交互社群的方法。