Data Quality / Governance Stack (Great Expectations, dbt, Airflow, PostgreSQL, Python) overview

A data quality and governance platform that embeds validation checkpoints throughout the data pipeline. dbt models define tested transformations with built-in schema and data tests. Airflow orchestrates pipeline DAGs with quality gates that halt downstream processing on validation failure. Python scripts run custom quality rules — completeness checks, referential integrity, distribution drift detection — and publish results to a PostgreSQL-backed quality dashboard. SQLAlchemy provides the data access layer. Designed for data teams in regulated industries where data quality is auditable and pipeline failures must be traceable to specific validation rules. The tradeoff is pipeline velocity — adding quality gates increases end-to-end latency and requires balancing thoroughness against freshness SLAs for downstream consumers.