The field of data compression and sensing is moving towards innovative solutions that leverage tensor-based methods, analog compressed sensing, and learned codecs. Recent developments have shown that standard SVD-based compression approaches can be effective for spatiotemporal data, while approximate proximal operators can be realized using electric analog circuits. Furthermore, learned codecs tailored to Earth observation have achieved significant compression ratios, outperforming classical codecs. Noteworthy papers include TerraCodec, which introduces a family of learned codecs for Earth observation, and A Dimension-Keeping Semi-Tensor Product Framework for Compressed Sensing, which proposes a novel method for compressed sensing that leverages intra-group correlations. These advancements have the potential to enable more efficient storage and transmission of large datasets, and improve the reconstruction performance of sparse signals.