Data engineering workflows require reliable differencing across files, databases, and query outputs, yet existing tools falter under schema drift, heterogeneous types, and limited explainability. SmartDiff is a unified system that combines schema-aware mapping, type-specific comparators, and parallel execution. It aligns evolving schemas, compares structured and semi-structured data (strings, numbers, dates, JSON/XML), and clusters results with labels that explain how and why differences occur. On multi-million-row datasets, SmartDiff achieves over 95 percent precision and recall, runs 30 to 40 percent faster, and uses 30 to 50 percent less memory than baselines; in user studies, it reduces root-cause analysis time from 10 hours to 12 minutes. An LLM-assisted labeling pipeline produces deterministic, schema-valid multilabel explanations using retrieval augmentation and constrained decoding; ablations show further gains in label accuracy and time to diagnosis over rules-only baselines. These results indicate SmartDiff's utility for migration validation, regression testing, compliance auditing, and continuous data quality monitoring. Index Terms: data differencing, schema evolution, data quality, parallel processing, clustering, explainable validation, big data

Schema evolution is critical in managing database systems to ensure compatibility across different data versions. A schema registry typically addresses the challenges of schema evolution in real-time data streaming by managing, validating, and ensuring schema compatibility. However, current schema registries struggle with complex syntactic alterations like field renaming or type changes, which often require significant manual intervention and can disrupt service. To enhance the flexibility of schema evolution, we propose the use of generalized schema evolution (GSE) facilitated by a compound AI system. This system employs Large Language Models (LLMs) to interpret the semantics of schema changes, supporting a broader range of syntactic modifications without interrupting data streams. Our approach includes developing a task-specific language, Schema Transformation Language (STL), to generate schema mappings as an intermediate representation (IR), simplifying the integration of schema changes across different data processing platforms. Initial results indicate that this approach can improve schema mapping accuracy and efficiency, demonstrating the potential of GSE in practical applications.