Data-intensive applications involving the analysis of large datasets often require large amounts of compute and storage resources, for which locality can be crucial to high throughput and performance. We propose a data approach that acquires compute and storage resources dynamically, replicates in response to demand, and schedules computations close to data. As demand increases, more resources are acquired, thus allowing faster response to subsequent requests that refer to the same data; when demand drops, resources are released. This approach can provide the benefits of dedicated hardware without the associated high costs, depending on workload and resource characteristics. To explore the feasibility of diffusion, we offer both a theoretical and an empirical analysis. We define an abstract model for diffusion, introduce new scheduling policies with heuristics to optimize real-world performance, and develop a competitive online cache eviction policy. We also offer many empirical experiments to explore the benefits of dynamically expanding and contracting resources based on load, to improve system responsiveness while keeping wasted resources small. We show performance improvements of one to two orders of magnitude across three diverse workloads when compared to the performance of parallel file systems with throughputs approaching 80 Gb/s on a modest cluster of 200 processors. We also compare diffusion with a best model for active storage, contrasting the difference between a pull-model found in diffusion and a push-model found in active storage.