Data engineering teams often struggle with managing Flink SQL jobs in production, lacking the robust CI/CD pipelines common in backend development. This article addresses that gap by proposing a workflow that brings validation, traceability, rollback, and automation to Flink SQL development. The approach treats Flink SQL scripts as code, integrating them into standard DevOps pipelines for testing and deployment. Key benefits include reduced manual errors, faster iteration cycles, and improved collaboration between data and platform teams. For organizations running real-time data warehouses on Flink, adopting such practices can significantly enhance reliability and developer productivity. The concept is particularly relevant as real-time analytics becomes more critical for business operations.
This article discusses how to apply CI/CD practices to Flink SQL real-time data warehouse development, enabling validation, traceability, rollback, and automation similar to Java backend workflows.