Finding new Information and patterns in Big Data is the new imperative. Large-scale data-driven applications are on the rise, and so is the need to extract “rare-events” from terabyte and petabyte-scale data sets (Big Data). Additionally, the resolution of data from the large scientific instruments are increasing. Visualizing multi-dimensional, time-varying datasets is both a challenge to the computational infrastructure and to the current display technologies. A single computer monitor can constrain the way in which we can analyze large volumes of data events. We can view either a low-resolution image that represents the entire extent of the data events and thus risk missing the detail, or we can view small portions of the data events in high resolution while losing the full context. In order to understand the complex Big Data analysis environments for extracting new information, we need advanced visualization and analytics tools for Big Data environments. With Hiperwall, we can see both the broad view of the datasets and the details concurrently, enabling shared viewing of complex results. With our Hiperwall Visualization system, we can display extremely large images, conduct interactive analysis of large datasets, view multiple images or parameters of large datasets, conduct a comparative view of many data sets (multiple data events) at once, and stream videos from remote collaborators (e.g. from CERN).