By implementing machine learning at the network edge, edge learning trains models by leveraging rich data distributed at edge devices and in return endow on them capabilities of seeing, listening, and reasoning. In edge learning, the need of high-mobility wireless data acquisition arises in scenarios where edge devices (or even servers) are mounted on ground or aerial vehicles. In this letter, we present a novel solution, called fast analog transmission (FAT), for high-mobility data acquisition in edge-learning systems, which has several key features. First, FAT incurs low-latency. Specifically, FAT requires no source-and-channel coding and no channel training via the proposed technique of Grassmann analog encoding (GAE) that encodes data samples into subspace matrices. Second, FAT supports spatial multiplexing by directly transmitting analog vector data over an antenna array. Third, FAT can be seamlessly integrated with edge learning (i.e., training of a classifier model in this letter). In particular, by applying a Grassmannian-classification algorithm from computer vision, the received GAE encoded data can be directly applied to training the model without decoding and conversion. This design is found by simulation to outperform conventional schemes in learning accuracy at a moderate-SNR range under the high mobility scenario due to its robustness against data distortion induced by fast fading.