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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2605.20500 |
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| _version_ | 1866911700015382528 |
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| author | Gargouri, Ismail Reza, Hassan |
| author_facet | Gargouri, Ismail Reza, Hassan |
| contents | Ensuring data quality in cloud-native Extract-Load-Transform (ELT) pipelines is increasingly challenging due to heterogeneous data sources, evolving schemas, and multi-backend execution environments. This paper presents a unified, multi-layer testing framework that integrates orchestration-level validation, declarative dbt tests, large language model (LLM)-generated semantic tests, and cross-store consistency checking between DuckDB and Snowflake, orchestrated through Apache Airflow. Controlled anomaly-injection experiments demonstrate that a manual-only baseline detected 7 of 16 injected anomalies. In contrast, both a manually expanded comparator and the proposed LLM-augmented configuration detected all 16, representing a 128.57% relative improvement in detection rate over the baseline. Post-migration cross-store validation confirmed exact agreement across all three curated tables. Of 25 LLM-generated test assertions, 9 were classified as useful, 4 as redundant, and 12 as executable but low-value. The complete workflow executed in 106.58 seconds across eight instrumented pipeline stages. These results demonstrate that LLM-driven semantic test synthesis can materially strengthen validation coverage while remaining operationally practical for production ELT environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_20500 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Multi-Layer Testing Framework for Automated Data Quality Assurance in Cloud-Native ELT Pipelines Gargouri, Ismail Reza, Hassan Software Engineering Ensuring data quality in cloud-native Extract-Load-Transform (ELT) pipelines is increasingly challenging due to heterogeneous data sources, evolving schemas, and multi-backend execution environments. This paper presents a unified, multi-layer testing framework that integrates orchestration-level validation, declarative dbt tests, large language model (LLM)-generated semantic tests, and cross-store consistency checking between DuckDB and Snowflake, orchestrated through Apache Airflow. Controlled anomaly-injection experiments demonstrate that a manual-only baseline detected 7 of 16 injected anomalies. In contrast, both a manually expanded comparator and the proposed LLM-augmented configuration detected all 16, representing a 128.57% relative improvement in detection rate over the baseline. Post-migration cross-store validation confirmed exact agreement across all three curated tables. Of 25 LLM-generated test assertions, 9 were classified as useful, 4 as redundant, and 12 as executable but low-value. The complete workflow executed in 106.58 seconds across eight instrumented pipeline stages. These results demonstrate that LLM-driven semantic test synthesis can materially strengthen validation coverage while remaining operationally practical for production ELT environments. |
| title | A Multi-Layer Testing Framework for Automated Data Quality Assurance in Cloud-Native ELT Pipelines |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2605.20500 |