In a companion paper, a faceted wideband imaging technique for radio interferometry, dubbed Faceted HyperSARA, has been introduced and validated on synthetic data. Building on the recent HyperSARA approach, Faceted HyperSARA leverages the splitting functionality inherent to the underlying primal-dual forward-backward algorithm to decompose the image reconstruction over multiple spatio-spectral facets. The approach allows complex regularization to be injected into the imaging process while providing additional parallelization flexibility compared to HyperSARA. This paper introduces new algorithm functionalities to address real data sets, implemented as part of a fully fledged matlab imaging library made available on GitHub. A large-scale proof of concept is proposed to validate Faceted HyperSARA in a new data and parameter scale regime, compared to the state of the art. The reconstruction of a 15 GB wideband image of Cyg A from 7.4 GB of Very Large Array data is considered, utilizing 1440 CPU cores on a high-performance computing system for about 9 h. The conducted experiments illustrate the reconstruction performance of the proposed approach on real data, exploiting new functionalities to leverage known direction-dependent effects, for an accurate model of the measurement operator, and an effective noise level accounting for imperfect calibration. They also demonstrate that, when combined with a further dimensionality reduction functionality, Faceted HyperSARA enables the recovery of a 3.6 GB image of Cyg A from the same data using only 91 CPU cores for 39 h. In this setting, the proposed approach is shown to provide a superior reconstruction quality compared to the state-of-the-art wideband clean-based algorithm of the wscleansoftware.