Hi Amogh, Thanks for the feedback.
I am happy to change the provider name to dataquality. Regarding the LLM-assisted features, the current PR does not include any implementation. It only adds the SKILLS [1 ]and the reference schema for the DQ Rule structure. Are you suggesting that I move this SKILL documentation to a separate PR? [1]: https://github.com/gopidesupavan/airflow/blob/9dac869e30d7e1e35aa9297b3098f10667c42aba/providers/dq/src/airflow/providers/dq/skills/dq-rule-authoring/SKILL.md Regards, Pavan On Wed, Jul 8, 2026 at 9:48 AM Amogh Desai <[email protected]> wrote: > Hi Pavan, > > First of all, +1 to this. > > Now, few things: > > * On naming: dataquality over dq for me honestly. Our existing provider > names spell things out > (common.sql, openlineage, not abbreviated forms) and dq is genuinely > ambiguous outside context. > > * On scope: I also agree with Niko that #69413 is too large for one pass & > I am glad to see the > backend/UI split already happening in #69575. Would also suggest keeping > the LLM assisted rule > generation pieces (*schema-based generate_rules_from_schema*) out of the > initial provider PR entirely > cos as I see it, its a separable capability and bundling it will slow > review of the core DQRule or > RuleSet or operator surface, which is the part that actually needs the most > detailed review. > > In short: go for it! > > > Thanks & Regards, > Amogh Desai > > > On Mon, Jul 6, 2026 at 9:35 PM Pavankumar Gopidesu < > [email protected]> > wrote: > > > In the meantime, the PR is ready for review. Feel free to review and > > provide any feedback. > > > > Regards, > > Pavan > > > > On Sun, Jul 5, 2026 at 3:20 PM Pavankumar Gopidesu < > > [email protected]> > > wrote: > > > > > Sorry, I forgot to add: the draft PR is here > > > https://github.com/apache/airflow/pull/69413; it's still a WIP. > > > > > > some screenshots > > > https://github.com/apache/airflow/pull/69413#issuecomment-4886311468 > :) > > > > > > Pavan > > > > > > On Sun, Jul 5, 2026 at 3:15 PM Pavankumar Gopidesu < > > > [email protected]> wrote: > > > > > >> Hi Airflow community, > > >> > > >> I would like to start a discussion regarding a new provider: > > >> apache-airflow-providers-dq. > > >> > > >> While Airflow already includes SQL check operators that many users > rely > > >> on for data quality, this new provider builds on that foundation by > > >> introducing DQRule and RuleSet objects, stable rule identity, > persisted > > >> history, and direct connections to Airflow assets. This approach makes > > >> quality results easier to inspect over time, allows downstream > > consumers to > > >> gate tasks based on recent quality results, and provides a unified > > schema > > >> for LLM-assisted workflows. Execution will continue to utilize > existing > > >> DbApiHook connections. > > >> > > >> The initial version of the provider is intentionally focused: > > >> > > >> - Declarative DQRule and RuleSet objects. > > >> - DQCheckOperator and @task.dq_check. > > >> - DbApiHook-based SQL checks, including built-in checks and > > custom_sql. > > >> - Persisted results for tasks, runs, and rules. > > >> - A minimal Airflow UI plugin for viewing results and rule history. > > >> - Experimental asset helpers such as asset_quality() and > > >> require_quality(). > > >> > > >> Regarding scope, this first iteration uses object storage only to > > persist > > >> DQ results and history; checks are executed via database connections. > > >> Future iterations may include file or object-store based checks (e.g., > > S3, > > >> GCS) where Airflow runs quality rules against data directly. > > >> > > >> This proposal does not require changes to Airflow core. Asset support > is > > >> currently provider-owned metadata, with static configuration stored on > > the > > >> asset and runtime summaries stored on asset events. If the provider > > gains > > >> traction, we can discuss making Data Quality a first-class component > of > > >> Airflow assets. > > >> > > >> This work also serves as a practical follow-up to the data quality > > >> direction mentioned in AIP-99. Persisted history is valuable for users > > and > > >> future LLM-assisted workflows, such as those from Anthropic or > > common.ai, > > >> to understand rule performance and generate candidate rules based on > > schema > > >> context. > > >> > > >> A rough pseudo-flow is provided below: > > >> > > >> seed_rules = RuleSet( > > >> name="orders_quality", > > >> rules=[ > > >> DQRule(name="order_id_not_null", check="null_count", > > >> column="order_id", condition={"equal_to": 0}), > > >> DQRule(name="amount_valid", check="min", column="amount", > > >> condition={"geq_to": 0}), > > >> ], > > >> ) > > >> > > >> orders_asset = asset_quality( > > >> Asset("orders"), > > >> conn_id="warehouse", > > >> table="orders", > > >> ruleset=seed_rules, > > >> ) > > >> > > >> # Optional: common.ai / Anthropic provider can generate a RuleSet > from > > >> schema context. > > >> generated_rules = generate_rules_from_schema(...) > > >> > > >> @task.dq_check(asset=orders_asset) > > >> def check_orders(ruleset): > > >> return ruleset > > >> > > >> checked_orders = check_orders(generated_rules) > > >> > > >> with DAG("orders_consumer", schedule=orders_asset): > > >> require_quality(orders_asset, min_score=0.95) >> consume_orders() > > >> > > >> The UI remains deliberately minimal for this initial release, focusing > > on > > >> result and history inspection. > > >> > > >> You can view examples [1] of how it's integrated with assets/llms. > > >> > > >> currently i named it providers `apache-airflow-providers-dq`. if any > > >> other preference likely with `dataquality`. Please let me know if you > > have > > >> a preference. naming is hard :) > > >> > > >> [1]: > > >> > > > https://github.com/gopidesupavan/airflow/blob/52b447f7acfbae6bd8673e87a2b40098aee3e6fb/providers/dq/src/airflow/providers/dq/example_dags/ > > >> > > >> Thanks, > > >> Pavan > > >> > > >> > > >
