Big data and the knowledge we glean from it are fundamentally changing the way in which resource management decisions are being made. While the use of remotely sensed data, spatial modeling, and newer processing techniques are helping to provide managers with depictions of ecosystems at unprecedented spatial and temporal resolutions, the sheer amount of data currently being collected has outpaced our abilities to efficiently manipulate and use those data. Newer tools, algorithms, and approaches are needed to address processing limitations and provide new opportunities to embrace the volume, variety, and velocity of big data streams. Important questions related to scale, data relevance, how to transform data into useful information, and the types of tools needed to efficiently manipulate data for natural resource management are at the forefront of the decision-making process. In this presentation we highlight new and novel approaches to processing spatial data and present three use cases that efficiently use big data streams to quantify multiple aspects of the initial forest and fuels condition while simultaneously quantifying costs of implementing various strategies to reduce wildfire risk and increase forest resilience.