Python Framework Automates Enterprise Data Validation Across ETL Pipelines

A layered, configuration-driven data validation framework built in Python aims to replace manual verification in large-scale data migrations, ETL pipelines, and multi-database reconciliations. The framework allows engineering and QA teams to define test cases through YAML files, supporting four validation types: row count checks, cell-by-cell data comparison, numeric reconciliation, and flat file comparison. It follows a two-level control architecture, where both group-level and test-case-level settings must be enabled for a test to execute. SQL queries are stored in external files rather than inline configurations to maintain clean version control and avoid schema drift. The framework also auto-generates HTML summary reports and maintains rotating logs, providing audit trails and executive-level visibility into data quality outcomes.