Adaptive AI-Driven Enterprise Integration Framework: Intelligent Schema Mapping and Predictive Quality Management Flow

Abstract

This scholarly article examines the transformative impact of DataOps methodologies and modern data architectures on enterprise data engineering practices. Beginning with an exploration of how DataOps has evolved from traditional workflows by incorporating Agile principles, the article compares the strengths and limitations of data warehouses, data lakes, and the emerging lakehouse paradigm. The article delves into critical data modeling strategies—star schema, snowflake schema, and Data Vault—evaluating their performance characteristics and suitability for different organizational contexts. Through detailed case studies across financial services and other industries, the research documents concrete benefits of DataOps implementation and architectural modernization, including reduced time-to-insight and improved data quality. The article concludes by identifying emerging trends in AI/ML integration with DataOps frameworks, highlighting significant research gaps in data engineering methodology, and offering practical recommendations for organizations at various stages of data maturity.