To address the increased latency, network load and compromised privacy issues associated with the Cloud-centric IoT applications, fog computing has emerged. Fog computing utilizes the proximal computational and storage devices, for sensor data analytics. The edge-fog-cloud continuum thus provides significant edge analytics capabilities for realizing interesting IoT applications. While edge analytics tasks are usually performed on a single node, distributed edge analytics proposes utilizing multiple nodes from the continuum, concurrently. This paper discusses and demonstrates distributed edge analytics from three different perspectives; serverless data pipelines (SDP), distributed computing and edge analytics, and federated learning, with our frameworks, MQTT based SDP, CANTO and FIDEL, respectively. The results produced in the paper, through different case studies, show the feasibility of performing distributed edge analytics following the three approaches, across the continuum.