+1 Good design/ idea. No objections - dataquality is a good name - but I would also consider `common-dataquality" - even if it's longer, it builds on the pattern we have already with common-ai. But not a blocker.
I also think it's good to have it as a separate provider, even if it gains traction for two reasons: a) ability to add features or fix issues independently from the core b) an explicit "optional" feature that is easy to promote I think what we saw with common is that people see airflow already as too heavy - and "too many releases" sometimes, so quite counter-intuitively - by having separate providers adding features that "hook in" existing functionalities of core - we do not make airflow "heavier" and we do not force people to migrating to future newer versions to use new features. J. On Wed, Jul 8, 2026 at 11:04 AM Pavankumar Gopidesu <[email protected]> wrote: > 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 > > > >> > > > >> > > > > > >
