Research on Real-time Processing and Stream Analysis of Unstructured Data Based on Big Data Platforms

With the rise of the big data era, massive streams of data are emerging in various fields such as the internet, Internet of Things, and finance, posing significant challenges to real-time processing. These data exhibit characteristics such as high velocity, randomness, and disorderliness, making it imperative to ensure the timeliness, stability, and correctness of systems. Therefore, this paper focuses on the stream processing of unstructured massive data on big data platforms and constructs a stream data capture and analysis architecture based on distributed processing mechanisms. Firstly, this paper designs a distributed stream data processing system based on network data capture, specifically targeting high-speed data processing requirements. By utilizing Pcap network data capture technology in conjunction with distributed computing systems, real-time high-speed data parsing is achieved. Meanwhile, this paper conducts a comparative analysis of two common distributed frameworks, establishing frameworks that support high-speed data storage and stream computing, thereby avoiding the problems of insufficient single-machine computing capacity and high costs of large-scale computers. Secondly, in the case where data from different business systems have different protocol contents, this paper proposes a generic protocol parsing method. By defining template writing rules and constructing corresponding protocol parsing template files, parsing of generic protocols is achieved. Through experimental validation, this paper verifies the feasibility of the proposed methods, providing important references for the real-time processing and stream analysis of unstructured data on big data platforms.