Today's sensing and processing algorithms operate on vast data streams coming from a broad range on input sources. In response, embedded computing applications require a large degree of configurability and adaptability to operate on a variety of data inputs where the characteristic of the data inputs may also change over time. To address these challenges, runtime reconfigurable systems can enable efficient implementations in which hardware accelerators can be reconfigured in response to the characteristics of the current data inputs. In this paper, we present an overview of the framework and runtime reconfiguration methods developed in the data-adaptable reconfigurable embedded systems (DARES) project. We provide an overview of the rapid deployment and runtime reconfiguration capabilities of this methodology, showcasing an adaptable implementation of a JPEG2000 image compression application.