Research on Reliable Edge Data Analytics focuses on designing methods and frameworks that ensure accurate, timely, and dependable processing of data at the network edge, even in the presence of heterogeneous devices, dynamic workloads, network variability, and resource constraints. This area addresses challenges such as fault tolerance, data consistency, real-time decision-making, and Quality of Service (QoS) in edge computing environments. Key research directions include resilient data collection and aggregation methods, fault-tolerant and adaptive analytics frameworks, and predictive models for proactive anomaly detection. Other emerging topics involve distributed and collaborative edge–cloud analytics, energy- and latency-aware data processing, and AI-driven reliability enhancement for real-time IoT and mobile applications. Additionally, research on secure and privacy-preserving reliable analytics, multi-objective optimization for reliability, latency, and resource utilization, and robust edge data pipelines represents significant avenues for advancing dependable, efficient, and intelligent edge computing systems.