This article presents an edge computing architecture for data preprocessing using DolphinDB, a high-performance time-series database. It covers how to set up edge nodes to filter, aggregate, and transform data locally before transmitting it to the cloud, reducing latency and bandwidth usage. Key topics include data ingestion pipelines, stream processing, and integration with cloud databases. The approach is particularly relevant for IoT deployments where real-time decision-making is critical. By preprocessing data at the edge, organizations can improve system responsiveness and reduce cloud costs. The post includes architectural diagrams and configuration examples, making it a practical reference for engineers designing edge computing solutions. This architecture is scalable and can be adapted to various industrial IoT scenarios.
An architectural overview of edge data preprocessing using DolphinDB, with practical insights for IoT and real-time analytics.