Edge computing enables data processing and storage closer to where the data are created. Given the largely distributed compute environment and the significantly dispersed data distribution, there are increasing demands of data sharing and collaborative processing on the edge. Since data shuffling can dominate the overall execution time of collaborative processing jobs, considering the limited power supply and bandwidth resource in edge environments, it is crucial and valuable to reduce the communication overhead across edge devices. Compared with data compression, compact data structures (CDS) seem to be more suitable in this case, for the capability of allowing data to be queried, navigated, and manipulated directly in a compact form. However, the relevant work about applying CDS to edge computing generally focuses on the intuitive benefit from reduced data size, while few discussions about the challenges are given, not to mention empirical investigations into real-world edge use cases. This research highlights the challenges, opportunities, and potential scenarios of CDS implementation in edge computing. Driven by the use case of shuffling-intensive data analytics, we proposed a three-layer architecture for CDS-aided data processing and particularly studied the feasibility and efficiency of the CDS layer. We expect this research to foster conjoint research efforts on CDS-aided edge data analytics and to make wider practical impacts.