Airflow Data Quality Provider

[gopidesupavan]

Adds a new apache-airflow-providers-dq provider,
DbApiHook-based data quality checks.

Airflow already has SQL check operators, and many users rely on them for data
quality today. This provider does not replace that path; it adds a small
DQRule / RuleSet layer for checks that need stable rule identity, persisted
history, and a connection to Airflow assets. That makes quality results easier
to inspect over time, lets downstream asset consumers gate on recent quality,
and also gives LLM-assisted workflows one schema to generate when proposing
checks from table context. Execution still goes through existing DbApiHook
connections.

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, plus custom_sql 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.
• A read-only API and minimal UI plugin for viewing task/run results and rule
  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.
• The provider addiitonally ships with dq authoring skills, that LLM's can use to generate rules based on the users need. SKILLS https://github.com/gopidesupavan/airflow/blob/f32940bd261b94238256eaced9150dd51329ce3e/providers/dq/src/airflow/providers/dq/skills/dq-rule-authoring/SKILL.md

This first version is intentionally small. It focuses on a deterministic rule
shape, SQL execution through common.sql, persisted results, and lightweight
visibility in the Airflow UI.

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/local files via [dq] results_path.
  The backend stores keyed JSON records for task runs, task instances, and
  per-rule history so the UI can read common views without scanning unrelated
  runs.
• Asset support is implemented by extending assets with provider-owned metadata,
  not by changing Airflow core. Static quality configuration is attached to
  Asset.extra["airflow.dq"]; runtime summaries are attached to asset events
  under extra["airflow.dq.result"]. This lets users try asset quality gating
  now, while leaving room to discuss deeper asset integration later if the
  provider gets traction.
• The first release starts with DbApiHook / SQL execution because Airflow
  already has strong provider coverage through common.sql. File and
  object-store data checks are left for a later iteration.

later iteration:

• 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. This PR deliberately starts with the DbApiHook path first.
• OpenLineage integration for data quality facets.
• More checks implementations
• Trigger support for DQ checks
• DQProfileOperator

---

Was generative AI tooling used to co-author this PR?

• Yes

Generated-by: following the guidelines

---

---

• Read the Pull Request Guidelines for more information. Note: commit author/co-author name and email in commits become permanently public when merged.
• For fundamental code changes, an Airflow Improvement Proposal (AIP) is needed.
• When adding dependency, check compliance with the ASF 3rd Party License Policy.
• For significant user-facing changes create newsfragment: {pr_number}.significant.rst, in airflow-core/newsfragments. You can add this file in a follow-up commit after the PR is created so you know the PR number.

[gopidesupavan]

LLM generated rules and executed via dq

Overall view of the task executed rules and run history

[o-nikolas]

This first version is intentionally small.

I'm not so sure that it is 😅 This is an absolutely enormous PR, 11.8k lines added across 143 files. This is quite difficult to review. Is it possible to ship this in smaller pieces?

[gopidesupavan]

This first version is intentionally small.

I'm not so sure that it is 😅 This is an absolutely enormous PR, 11.8k lines added across 143 files. This is quite difficult to review. Is it possible to ship this in smaller pieces?

hehe it has both UI and backend shipped thats the reason it looks like big.. and some docs 😄

I am happy to ship this into two separate PR's one with UI and another backend provider specific..

[gopidesupavan]

@o-nikolas here is the part 1 #69575 hope its fine 😄 now.. as this is provider starting so it has autogenrated breeze docs and content in documentation ..