A Multi-Layer Testing Framework for Automated Data Quality Assurance in Cloud-Native ELT Pipelines

2026-05-19

AI Summary

This study addresses the critical data quality challenges in cloud-native ELT pipelines arising from heterogeneous data sources, schema evolution, and multi-backend environments. The authors propose a multi-layered testing framework that integrates orchestration-level validation, declarative dbt tests, LLM-generated semantic tests, and cross-storage consistency verification between DuckDB and Snowflake, all unified under Apache Airflow scheduling. Notably, this work pioneers the incorporation of LLM-driven semantic testing into production-grade ELT workflows. In anomaly injection experiments, the framework achieves a 128.57% improvement in detection rate, successfully identifying all 16 injected anomalies. Furthermore, three critical tables exhibit perfect consistency across both storage systems, with the end-to-end pipeline completing in just 106.58 seconds, substantially enhancing both detection coverage and engineering feasibility.

Abstract

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.

Problem

• data quality
• cloud-native ELT pipelines
• heterogeneous data sources
• evolving schemas
• multi-backend environments

Innovation

• multi-layer testing framework
• LLM-generated semantic tests
• cloud-native ELT pipelines
• cross-store consistency checking
• automated data quality assurance