gopidesupavan opened a new pull request, #69575:
URL: https://github.com/apache/airflow/pull/69575
Adds a new `apache-airflow-providers-dq` provider for `DbApiHook`-based
data quality checks.
Airflow already has SQL check operators, and many users rely on them for
data quality today. This provider adds a `DQRule` / `RuleSet` layer for checks
that need stable rule identity, persisted history, and a connection to Airflow
assets. That makes quality results easier to analyze over time, lets downstream
asset consumers gate on recent quality, and gives LLM-assisted workflows one
schema to generate when proposing checks from table context. Execution still
goes through existing `common.sql` / `DbApiHook` connections.
This PR is the backend/provider slice only. The UI plugin and read-only API
are intentionally left for a follow-up PR.
Ships:
- `DQRule` and `RuleSet` models for named data quality rules.
- Built-in SQL checks for common table and column checks, executed through
`common.sql` / `DbApiHook`.
- `custom_sql` support for database-specific or more complex checks.
- `DQCheckOperator` and the `@task.dq_check` TaskFlow decorator.
- A configurable results backend under `[dq] results_path` for task, run,
and rule-level history.
- Experimental asset helpers, `asset_quality()` and `require_quality()`,
that attach provider-owned quality metadata to assets without changing Airflow
core.
- Documentation and example Dags covering end-to-end usage with and without
LLM-generated rules.
- A DQ rule-authoring skill that LLM-assisted workflows can use to generate
rules from table/schema context:
https://github.com/gopidesupavan/airflow/blob/f32940bd261b94238256eaced9150dd51329ce3e/providers/dq/src/airflow/providers/dq/skills/dq-rule-authoring/SKILL.md
This first version is intentionally focused on the backend contract:
deterministic rule definitions, SQL execution through existing Airflow SQL
providers, persisted results, and asset-linked quality summaries.
Design decisions:
- Results are stored through an object-storage/local-file backend instead of
adding new metadata DB tables in the first provider drop. This keeps the
provider self-contained, avoids Airflow core migrations, and lets deployments
choose a durable store such as S3, GCS, or local files via `[dq] results_path`.
- The backend stores keyed JSON records for task runs, task instances, and
per-rule history so later readers, including a future UI/API layer, can access
common views without scanning unrelated runs.
- Asset support is implemented with provider-owned metadata, not Airflow
core changes. Static quality configuration is attached to
`Asset.extra["airflow.dq"]`; runtime summaries are attached to asset events
under `extra["airflow.dq.result"]`.
- The first release starts with `DbApiHook` / SQL execution because Airflow
already has broad database coverage through `common.sql`. File and object-store
data checks are left for a later iteration.
Later iterations:
- Read-only API and minimal Airflow UI plugin for viewing task/run results
and rule history.
- File/object-store based checks, where Airflow reads data from S3/GCS/local
files or other object stores and runs quality rules directly against that data.
- OpenLineage integration for data quality facets.
- More built-in checks.
- Trigger support for DQ checks.
- `DQProfileOperator`.
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