Design and Implementation of a Real-time Processing Framework for Financial Big Data

With the increasing demand for real-time data processing in the financial field, how to efficiently process large-scale financial data has become an important topic. This paper designs and implements a real-time processing framework for financial big data based on a stream processing architecture. The framework integrates technologies such as Apache Kafka and Apache Flink to achieve efficient collection, transmission and processing of financial data. The framework uses distributed data storage solutions such as HBase and ClickHouse to ensure fast access and query of massive data. This paper introduces the system architecture, core module design and technology selection of the framework in detail, and improves the system throughput, reduces latency, and effectively optimizes CPU and memory resource utilization through performance optimization strategies. Performance test results show that the designed framework performs well in improving system processing capabilities and response speed, and has good scalability and stability, which can meet the needs of real-time financial data processing.