ANTLR-Based Complex SQL Data Lineage Parsing and Visualization

With the widespread adoption of complex SQL queries in modern data environments, traditional data lineage tools face challenges in parsing accuracy and coverage, struggling to meet the demands of fine-grained data management and increasingly stringent data regulations. To address this challenge, this paper proposes an ANTLR-based automated method for accurately parsing complex SQL queries and extracting fine-grained, column-level data lineage. The core contributions of this method include: constructing and optimizing ANTLR grammar rules adapted to complex SQL scenarios, implementing a column-level lineage extraction algorithm that accurately traces data transformation logic, and designing a corresponding visualization scheme to support data dependency exploration, impact analysis, and issue tracing. This method effectively enhances the accuracy and depth of data lineage analysis, providing strong technical support for enterprises to strengthen data governance, compliance auditing, optimize data quality, and accelerate data development and debugging processes.