Application Research of Big Data Real-Time Processing Framework Based on Spark in Industrial Data Stream Analysis

Abstract

To meet the real-time analysis requirements of massive heterogeneous data streams generated by the Industrial Internet of Things (IIoT) under Industry 4.0, especially in complex engineering environments such as electromagnetic testing, antenna measurement, microwave device monitoring, and communication equipment manufacturing, this paper proposes an industrial big data real-time processing framework based on Apache Spark. By integrating Spark Structured Streaming, Spark MLlib, and time-series data storage technology, a full-process architecture covering data collection, preprocessing, real-time computing, and visualization is constructed. Combined with three typical scenarios, including smart power plant equipment monitoring, new energy battery production, and intelligent machine tool processing, a multi-level performance improvement strategy is designed, involving dynamic partition pruning, Shuffle optimization, and edge preprocessing, including protocol parsing, preliminary outlier filtering, and data compression at the gateway level. The effectiveness of the framework is verified through multidimensional comparative experiments. The results show that, in scenarios with millions of collection points, the framework achieves a throughput of 89,000 records per second, controls end-to-end latency within 280 ms, improves equipment fault prediction accuracy to 94.2%, reduces storage costs by 70%, improves performance by more than 40% compared with traditional Spark solutions, and reduces comprehensive resource occupancy, including CPU, memory, and network I/O, by 25% compared with Flink solutions. This research provides a practical technical solution for industrial real-time data analysis and offers engineering reference for data-driven monitoring of electromagnetic test platforms, antenna measurement systems, and microwave communication equipment.