Automated Schema Drift Detection Using AI and Metadata Intelligence in Cloud Data Warehouses
Abstract
Modern cloud data warehouses continuously ingest heterogeneous, fast-evolving datasets, making schema drift one of the most persistent challenges in maintaining analytical accuracy and operational reliability. Schema drift occurs when structural changes—such as new attributes, altered data types, renamed fields, or deleted columns—appear in incoming data without prior notice. Traditional rule-based monitoring systems often fail to detect these changes in real time and lack adaptability when confronted with high-velocity, semi-structured, and unstructured data sources. This paper proposes an AI-driven, metadata-intelligent framework for automated schema drift detection in cloud data warehouses. The approach integrates machine learning–based anomaly detection, metadata lineage analysis, and semantic inference to identify schema variations with minimal human intervention. By leveraging pattern recognition models and metadata intelligence from catalogs, logs, and transformation histories, the system identifies drift occurrences and predicts potential future schema evolution. The framework supports multi-cloud architectures, enabling compatibility across platforms such as Snowflake, BigQuery, AWS Redshift, and Azure Synapse. Experimental evaluation demonstrates improved detection accuracy, reduced false positives, and faster remediation times compared to traditional monitoring methods. This paper concludes by highlighting the significance of AI-enabled metadata ecosystems for enhancing data reliability, operational resilience, and autonomous data engineering pipelines
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