{
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  "_kind": "StackProfile",
  "_file": "domain/stack-profiles/deep-stacks-4.yaml",
  "_cluster": "domain",
  "attributes": {
    "displayName": "Data Quality / Governance Stack (Great Expectations, dbt, Airflow, PostgreSQL, Python)",
    "description": "A data quality and governance platform that embeds validation checkpoints\nthroughout the data pipeline. dbt models define tested transformations\nwith built-in schema and data tests. Airflow orchestrates pipeline DAGs\nwith quality gates that halt downstream processing on validation failure.\nPython scripts run custom quality rules — completeness checks, referential\nintegrity, distribution drift detection — and publish results to a\nPostgreSQL-backed quality dashboard. SQLAlchemy provides the data access\nlayer. Designed for data teams in regulated industries where data\nquality is auditable and pipeline failures must be traceable to specific\nvalidation rules. The tradeoff is pipeline velocity — adding quality\ngates increases end-to-end latency and requires balancing thoroughness\nagainst freshness SLAs for downstream consumers.\n",
    "composes": [
      "language:python",
      "language:sql",
      "tool:airflow",
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