The talk covers recent research, conducted by the speaker and his PhD students, aimed at addressing the challenges of high-throughput, low-latency stream processing across the cloud–edge continuum. As centralized data collection becomes increasingly impractical due to the sheer volume of data, transitioning to approaches that push computation closer to the edge emerges as both a promising and necessary alternative. However, the limited and often shared resources available on edge devices pose significant challenges to this shift. After a brief introduction to core stream processing concepts, the talk will cover:(i) how the fragmented landscape of frameworks and processing paradigms can be unified and bridged through a minimal set of stream processing abstractions, better supporting heterogeneous deployments across the cloud–edge spectrum; (ii) the role of stream provenance in enabling more efficient data analysis and selection at the edge; and (iii) the benefits of solutions that trade resource utilization for performance in stream processing, with a particular focus on memory compression in streaming aggregates.