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1. Application Background

In the company's big data project, it is necessary to build and develop an efficient and reliable data processing subsystem to realize big data file processing, whole database migration, delay and disorder processing, data cleaning and filtering, real-time data aggregation, incremental synchronization (CDC), state management and recovery, back pressure problem processing, data sub-library and sub-table, cross-data source consistency, and real-time anomaly detection and alarm functions to ensure data accuracy, consistency and real-time performance. The data governance solution is adopted on the Spring Boot 3 and Flink platforms.

2. Advantages of the Solution

Since it is a big data project, file processing capabilities directly affect the effectiveness of data-driven decision-making when processing large-scale data sets. Efficient big data file processing must not only ensure the timeliness and accuracy of the data, but also improve the performance and reliability of the overall system.
Spring Boot 3. When used in combination with Flink, it has many unique advantages when processing large data files.
First of all, the two can complement each other and bring efficient and convenient file processing capabilities because:

（1）统一的开发体验：
Spring Boot 3. 和Flink结合使用，可以在同一项目中综合应用两者的优势。Spring Boot可以负责微服务的治理、API的管理和调度，而Flink则专注于大数据的实时处理和分析。两者的结合能够提供一致的开发体验和简化的集成方式。

（2）动态扩展和高可用性：
微服务架构下，Spring Boot提供的良好扩展性和Flink的高可用性，使得系统可以在需求增长时动态扩展，确保系统稳定运行。Flink的容错机制配合Spring Boot的服务治理能力，可以有效提高系统的可靠性。

（3）灵活的数据传输和处理：
通过Spring Boot的REST API和消息队列，可以轻松地将数据传输到Flink进行处理，Flink处理完毕后还可以将结果返回到Spring Boot处理的后续业务逻辑中。这种灵活的处理方式使得整个数据处理流程更为高效且可控。

3. Implementation steps

1. First, configure the development environment of Spring Boot 3.x and Flink. Add the necessary dependencies in pom.xml:

                                   

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