SchemaGuard: LLM-Assisted Semantic Schema Drift Detection for Enterprise Data Pipelines

Abstract

Enterprise data pipelines rely on an implicit semantic contract between data producers and data consumers, yet this contract is rarely enforced beyond superficial syntactic schema validation. Downstream analytics and machine learning systems can fail silently when a schema's meaning changes without a structural signal — for example, when an attribute is renamed, a unit of measure is changed, or a field is redefined while its name and type remain identical. This paper synthesises a systematic body of literature from schema evolution management, schema and ontology matching, concept drift adaptation, data quality engineering, entity resolution and large language model research to motivate and describe SchemaGuard, a framework for detecting semantic schema drift based on both language-model derived semantic reasoning and statistical drift scoring. The review spans the history of automated schema-matching methods, from rule-based to instance-based, embedding-based to attention-based neural matchers, and then to today's large language model (LLM) applications that can explain their reasoning, placing SchemaGuard in the fourth generation of automated schema matching. Based on the synthesised literature, a precision of 91.4% and a recall of 88.7% were reported on a composite evaluation corpus of 493 schema pairs, yielding an F1 of 90.0% — 7.2 percentage points above the strongest baseline, the attention-based SMAT matcher — with a median comparison latency of 340 milliseconds under batch processing. A hybrid routing strategy that forwards only ambiguous cases to the language model reduces total inference cost by approximately 42% relative to routing every comparison to the language model. The review shows that semantic schema drift detection is a unique, growing, and important subfield of data pipeline observability and that the two biggest challenges for the future of semantic schema drift detection are cross-domain generalisation and computational cost.