Big data is more than a matter of size; it is an emerging paradigm of data of very large size (volume) and fast in/out (velocity), from various sources (variety), and of high value for knowledge extraction and decision making. Technological advances in data gathering have led to a rapid proliferation of big data in diverse areas such as remote sensing, medicine, the Internet, and social sectors. Such data brings opportunities and challenges to scientists and engineers. In order for us to make use of this massive amount of data, new data management and computational approaches are needed to permit scientists and engineers to analyze the data in (nearly) real time, often in a distributed or streaming manner. Various technologies are being discussed, and some have been realized, to support the handling of big data. In addition, big dataset cannot be stored in one location, and massively parallel processing databases and scalable storage systems are being designed to store the large datasets. What is more, big data generates an industry of supporting architectures; many cloud computing platforms and frameworks are developed to handle the big data operations, such as MapReduce. To deal with different properties of big data, different algorithms also need to be developed. Overall, big data is an opportunity to find insights in new and emerging types of data and content, to make models more agile, and to answer questions that were previously considered beyond our reach. The purpose of this special is to highlight some recent advancement to address such challenges in the big data era.