Granular/symbolic data processing is an emerging conceptual and computing paradigm of information processing. In the era of big data, the emergence of granular/symbolic processing has been motivated by the urgent need for intelligent transformation of empirical data that are now commonly available in vast quantities, into a human-manageable knowledge. In such an aggregation process, we hope to retain as much information as possible while making the findings easily understood and well-supported by the existing experimental evidence. Those aggregated entities are often referred to as symbolic or granular data. Research areas referred to as symbolic data analysis in statistics and multivariate data analysis address some of the fundamental or applied facets of granular computing. The theoretical fundamentals of granular/symbolic data processing are well-established. They involve set theory (interval mathematics), fuzzy sets, rough sets, and random sets linked together in a highly comprehensive treatment of this emerging paradigm. In addition to interval-based formalism of information granules, we also encounter histograms, distributions, lists of values, etc. Hence, granular/symbolic data processing hinges on a general computation theory that effectively uses granules such as classes, clusters, subsets, groups, and intervals to build an efficient computational model for complex applications realized in the presence of huge amounts of data, information, and knowledge. This research arises as a substantial shift from the current machine-centric to human-centric approach to information and knowledge.