Semantic-Aware Data Lineage Tracking for Transparent ETL Pipelines in Industrial Systems

In the context of Industry 4.0, data pipelines are increasingly central to the integration of systems, decision support, and real-time industrial automation. One critical capability is understanding and tracing how data flows and transforms through ETL (Extract, Transform, Load) stages. Existing approaches provide limited semantic visibility, relying mostly on logs, static DAGs, or limited metadata tagging. This paper presents a practical and reproducible implementation of a semantic-aware data lineage tracking system, using graph modeling (Neo4j), ontology representation (RDF), and semantic querying (SPARQL). We detail the design, implementation, and experimental validation of the pipeline, using the AdventureWorks DW2022 dataset. Results show the effectiveness of the model in enhancing traceability, querying flexibility, and integration-readiness for industrial-scale deployments.