Reliable edge data analytics is a growing research area that focuses on ensuring accurate, timely, and consistent processing of data at the edge of the network, especially in distributed and resource-constrained environments. Research papers in this domain explore methods for fault-tolerant data collection, processing, and aggregation, aiming to maintain service continuity and data integrity despite network failures, node crashes, or fluctuating workloads. Studies highlight the integration of machine learning, deep learning, and predictive analytics to enable real-time insights while maintaining reliability and low latency for applications such as autonomous vehicles, industrial IoT, smart healthcare, and smart cities. Recent works also investigate hierarchical edge–fog–cloud architectures, load balancing, workload-aware scheduling, and redundancy mechanisms to enhance resilience and reliability in edge analytics. Security- and privacy-preserving frameworks, including federated learning and blockchain integration, are also considered to protect sensitive data without compromising analytics accuracy. Overall, reliable edge data analytics research emphasizes designing intelligent, adaptive, and resilient frameworks that ensure high-quality, trustworthy, and continuous data-driven decision-making in next-generation distributed systems.