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feat(databricks): data quality monitoring from a contract's freshness and completeness SLOs - #864
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… and completeness SLOs An expose bound to Databricks that states a freshness or completeness SLO (qos.freshnessSLO, qos.completenessTarget, an observability.defaultSLIs freshness or completeness block, or a contract.dq.rules entry of type freshness, completeness or anomaly_detection) turns on Databricks data quality monitoring (formerly anomaly detection) for the schema the product owns: one databricks_data_quality_monitor per schema (object_type "schema", object_id = the schema's schema_id), created after the schema's tables so the first scan finds them, with the product's other tables there in excluded_table_full_names. contract.dq.monitoring.enabled: false opts an expose out. No new schema field and no new CLI flag. The monitor learns each table's bounds and takes no threshold (provider 1.140.0: excluded_table_full_names is its only setting), so the SLOs pick the tables; the apply notice says so, and fluid validate warns about a dq.monitoring.window other than one day. A monitor turned on outside forge-cli is adopted, not created over (a second create is 400 RESOURCE_ALREADY_EXISTS). Its import id is "schema,<schema_id>", a UUID Databricks assigns, so ImportBlock gains id_from: the engine completes the id from state after the schema's own import and skips it while the schema is not there. Removing a monitor deletes its hidden scan job and what it learned, never table data: it joins RUN_DEFINITION_RESOURCE_TYPES (not gated, printed). Proven live on Databricks Free Edition 2026-11-12 (scratch catalog fluid_iactest_dqm, dropped after): apply +14, an empty second plan, a table_results row (Training) for the watched table and none for the excluded one, Catalog Explorer's Quality indicator, adoption of a REST-made monitor, and an ungated opt-out. Borrowed: the provider's databricks_data_quality_monitor resource (no REST code); learned-vs-stated thresholds as complements follows Elementary's freshness_anomalies beside data_freshness_sla.
…e job actions are
…ion leaving converges Review round 1 of the data quality monitoring change. - When Databricks refuses the monitor (no serverless compute, no MANAGE on the schema), tofu apply fails after the schema's tables exist. The engine's _apply_failure now asks the plugin for hints on the failed resource (optional apply_failure_hints(address, detail), documented in iac/base.py); the Databricks plugin answers for a databricks_data_quality_monitor with what it needs and the opt-out, contract.dq.monitoring.enabled: false. The event stays opentofu_apply_failed. - The last excluded table starting to ask shrinks the exclusion list to nothing. Provider 1.140.0 PATCHes update_mask=anomaly_detection_config, data_profiling_config with an empty config; measured live on Free Edition 2026-11-12, the list is gone and the next plan is empty. The stand-in now honours update_mask, and a test pins the request shape. - The apply notice says Quality indicator, Catalog Explorer's own label, and names the opt-out; the window warning says what to do. - The destroy-order test retries one tofu destroy on a transport error: the stand-in closes every connection (HTTP/1.0), and under load the provider can write to one it closed; state keeps the order.
…oo; changelog rewrapped
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…quality-monitoring # Conflicts: # HONESTLY_TESTED.md # tests/iac/_fake_databricks.py
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…866) Found while documenting #864 for forge_docs. - Alerts: provider 1.140.0 does have notification_settings, but only under data_profiling_config (on_failure); anomaly_detection_config has only excluded_table_full_names. HONESTLY_TESTED said the provider had none. - A refused monitor's hints do not come first: CLIError's three generic suggestions come before them, and they go ahead of the engine's own tofu state list hint. CHANGELOG, iac/base.py and the _apply_failure docstring now say that. - The Free Edition quota covering the scans was assumed, not measured: the CHANGELOG now says to expect them to count against it.
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…onitoring #864 turns Databricks data quality monitoring on for an expose with an anomaly_detection rule, and the monitor learns the rule's bounds, which a dbt test cannot. The job's tests therefore leave such a rule to it (databricks_quality.monitoring_request), as agreed with that change. An expose that turns monitoring off (contract.dq.monitoring.enabled: false) still has its anomaly rule refused, and the fix names the opt-out. drift_detection stays refused.
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…#871) * feat(dq): read a contract's DQ rules once, as checks or refusals fluid_build/util/dq_checks.py turns exposes[].contract.dq.rules into engine-neutral checks (not_null, unique, accepted_values, regex, bounds, reference, freshness, schema) and a refusal, with its fix, for each rule no check can run as written. The allowed values of a valid_values rule come from the selected column: its validationRules (enum 'A | B' or 'A,B', regex, range) or labels.references <product>.<column> through consumes[]. Thresholds below 1.0 are shares computed as fluid test computes them; critical/error map to error, warn/info to warn. Shared with the Databricks DQ sessions (monitoring, results table, Lakeflow expectations) as the one reading of the rules. * feat(databricks): a dbt job runs its contract's DQ rules as dbt tests Each check of the rules on the exposes a Databricks dbt build writes is a singular dbt test, <test path>/fluid_dq/<model>/<check>.sql, uploaded beside the project; the user's files upload byte for byte (dbt describes a model in one properties entry only). dbt build runs each test after its model, so the run page lists every rule by id and an error-severity failure fails the run. not_null, unique, accepted_values and relationships run dbt's built-in test SQL through adapter.dispatch; regex, bounds, freshness, schema and shares are forge-cli's own SQL after dbt_utils (recency, not_null_proportion) and dbt_expectations (match_regex, table_columns_to_match_set), both Apache-2.0, with no package to install on serverless. Every value reaches the file as an escaped Jinja string and every SQL literal through databricks_access.string_literal. A rule no test can run, a Git-sourced build with rules, a model or a referenced source the project lacks are refused at validate and plan. The plan lists each job's tests (dq_tests); report() reads dbt's result lines from a run's output. * feat(dq): a reference to a product not consumed keeps no expose A source-aligned product consumes nothing, so a labels.references to another product's column no longer refuses in the shared reader: Reference.expose_id is None and the engine decides whether it can find that table (the Databricks dbt job cannot, and refuses). Two exposes of one consumed product stay refused as ambiguous. Each refusal carries the rule's type, so an engine that runs a type another way (Databricks data quality monitoring for anomaly_detection) can tell its own. * feat(databricks): the dbt job refuses an unconsumed reference; a run's report covers rules dbt stopped before The dbt test of a reference reads the other product from the dbt source the project declares for the expose it consumes, so a reference to a product consumes[] does not list is refused there. report() says once when the task's output holds no result of any rule's test (dbt stopped before them). The job's dbt task key is a constant, DBT_TASK_KEY, which a run's report finds the dbt output under. * fix(databricks): the schema test refs its model while dbt parses dbt records a test's parents while it parses, with execute false, and dbt build --select +model+ selects tests by them. The schema test called ref() only inside its execute guard, so it had no parent and was never selected: live on Databricks Free Edition dbt found 11 tests and ran 10. The relation is now set before the guard. Every kind is pinned to ref its model at parse time. * docs(databricks): changelog entry and the live row for DQ rules as dbt tests The Unreleased entry says what runs for each rule type, what is refused, and what fluid apply prints. HONESTLY_TESTED records the unit tiers and the manual live run on Databricks Free Edition (2026-11-12): a seeded bad row failed the run on its two error rules and warned on two, a clean run passed all 11 tests, the next plan was empty, and everything was swept; it also lists what was not run live. * refactor(dq): smaller units in the rule reader and the test compiler; two hardening fixes From the branch's security review and ripwire --quality-delta: - The schema test escapes the expected type as it escapes the type the warehouse reports. It is unreachable today (every Databricks column type passes _column_type first), but no longer depends on it (LOW). - A reference refusal quotes the labels.references parts it names (LOW). - compile_tests runs in stages (a _Project with its paths and lazily read sources, _test_folder, _add_test); _declared_sources reads through _properties_docs and _source_tables; _Rule._allowed reads each validationRule kind in its own method; read_rules and has_rules share _parts; report's wording is _outcome. - The job's tests ride on _Source, so _emit_job keeps its signature and its task dict stays one line. * feat(dq): each check keeps its rule's severity, threshold and operator as written DqCheck.rule_severity, rule_threshold and rule_operator carry the rule as the contract wrote it, beside the two-level severity a dbt test runs at and the share a lower threshold becomes: Lakeflow expectations fail an update on critical and drop the row on error, and a results table reports one row per rule. * fix(databricks): refuse a test name the project uses and a selection that skips the model From the diff's delivery review: - A rule's test is named after its id, and dbt names a singular test once per project: a project test of the same name made the whole job fail to parse ("dbt found two data_tests with the name"). It is refused at plan. - A build whose dbt selection provably leaves the model out would never run its tests: refused, with the model's fqn and path evaluated by databricks_retention.selects_model. A tag, config or state selection cannot be told here; the run's report names any test that did not run. - A real dbt (dbt-core with dbt-duckdb, as CI installs) parses the upload and dbt ls --select +orders+ lists every rule's test; with the schema test's ref put back behind its execute guard, it fails. - Wording: a share is fluid test's own only for completeness and uniqueness; the anomaly and drift refusal says what fluid test does with them; dq_checks is the reading for the engines that run the rules, this job its first. * feat(databricks): fluid apply reports what dbt found for each contract rule after a run Under --mode amend-and-build, once the job's run ends (success or failure), the run report reads the dbt task's output and prints one line per contract rule's test: pass, warn or fail with its failing-row count, error, skip, or not in the output (with the note when Databricks kept only its end), then the summary. The rules come from the contract alone (databricks_dbt_tests.expected_tests); the task is found by DBT_TASK_KEY, so the fluid_keys task beside it is never read as dbt's. runs/get-output is read once per task and feeds the dbt summary, the failed task's tail and the rules alike (_task_output). A rule report is printed for the dbt task when a later task failed too: its tests ran. Never fails a build: an unreadable output or contract only drops the report. * fix(dq): refuse a threshold or bound no float holds, and a range no value meets From the diff's defect scan: an integer threshold of hundreds of digits raised OverflowError out of fluid validate and plan, and so did a window too long to count; an infinite or NaN threshold, or a range bound past a float's range, reached the SQL as a bare 'inf'. Each is now a refusal (_finite, and parse_range drops a non-finite bound). A range no value can meet (between 100 and 0, > 5 and < 1, > 3 and <= 3) is refused instead of failing every row of its test. * fix(databricks): defect-scan fixes to the DQ tests and the keys task - Only the exposes with rules are tested, so only their models are looked for: a sibling output with no rules (no model of its name, or left out of the selection) was refused. - properties.model names the model of the one table a build writes; a build that writes several (no outputs writes every expose) tests each through the model named after its table. - fluid validate, which reads the contract before {{ env.* }} is substituted, no longer refuses a model named by a placeholder; the apply writes those tests. - The schema test spells a decimal as the table emitter does (_canonical_type): decimal(12) is decimal(12,0), as Databricks reports. - _jinja_str keeps characters outside the BMP whole: ensure_ascii wrote a surrogate pair, which dbt cannot encode (one emoji in a rule id failed the whole project's parse). - The test-name clash check also covers the project's own files under fluid_dq/, and a project YAML nested past the recursion limit is passed over like any file that does not parse. - The fluid_keys task (#860) runs with run_if ALL_DONE: a failing rule fails dbt after its model replaced the table, which dropped its keys. * docs(databricks): AGENTS.md and the live row for the DQ tests and their report AGENTS.md says the job runs the contract's DQ rules as dbt tests and where the reader and the renderer live; the changelog says the keys task now runs whatever dbt's outcome; HONESTLY_TESTED counts the tests again (48 + 54 + 4) and lists what the review waves added. * feat(databricks): an anomaly_detection rule is left to data quality monitoring #864 turns Databricks data quality monitoring on for an expose with an anomaly_detection rule, and the monitor learns the rule's bounds, which a dbt test cannot. The job's tests therefore leave such a rule to it (databricks_quality.monitoring_request), as agreed with that change. An expose that turns monitoring off (contract.dq.monitoring.enabled: false) still has its anomaly rule refused, and the fix names the opt-out. drift_detection stays refused. * fix(databricks): refuse an unpaired surrogate; a placeholder selection waits for the apply From the fix wave's re-check: - Since the Jinja strings keep raw Unicode, an unpaired surrogate (YAML's "\ud83d" escape loads one) in a rule id, an enum value or a regex raised UnicodeEncodeError out of fluid plan. The reader refuses one with the control characters, in ids too, and a value from the project's own YAML (a source name) that UTF-8 cannot hold is a refusal of its own. - fluid validate, before {{ env.* }} is substituted, refused a build whose properties.select still held a placeholder as leaving the model out; the selection is judged only once it can be read, at the apply. * docs(databricks): the anomaly hand-off to monitoring, and the counts, in the changelog and the live row * fix(databricks): refuse a test path no file can hold; monitoring takes over only the history refusal From the round-2 re-check: - An unpaired surrogate in dbt_project.yml's first test path (YAML's "\ud83d" escape loads one) reached the generated tests' paths and raised UnicodeEncodeError out of fluid plan. It is a test-path refusal now, and the message names the path escaped. - On an expose Databricks data quality monitoring watches, every refusal of an anomaly_detection rule was dropped, so a bad severity or an id holding a control character passed silently. DqRefusal.needs_history marks the one refusal monitoring takes over (the rule needs the table's history); the others are refused as before. * fix(databricks): a DQ refusal names a lone surrogate from the project escaped The round-3 re-check found model-paths reaching the model refusal's text raw, and a lone surrogate there (a YAML escape loads one) made fluid plan fail on printing the refusal. Every DQ refusal is built in _refusal, which now spells such a character as its backslash escape; a source or table name from the project's YAML in a reference refusal is covered the same way. * docs(databricks): the live row counts the round-3 tests and names the escaped refusal text * fix(dq): a valid_values rule allows what fluid test allows #867 landed fluid test's reading of a valid_values rule, providers/quality_engine.py::value_domain: the values the rule itself lists, else every declaration its column makes (the first enum, regex and range validationRule, and labels.references), all of which must hold. dq_checks had its own, which let the column's declarations win over the rule's list, split an enum on | only when it held one, and read every validationRule of a kind; the dbt tests could then check other values than fluid test does. dq_checks now takes the values, pattern and bounds from value_domain and keeps its refusals on top: a range fluid test cannot read (it passes one over) or no value can meet, a value or pattern holding a control character, and a labels.references that consumes[] cannot resolve. Its own parse_enum and parse_range are gone; the range grammar is quality_engine's. The Tower Management pack still reads as 330 checks with none refused, check for check as before, and the demo's dbt products as 230 tests. * docs(dq): valid_values reads as fluid test's value_domain, in AGENTS.md, the changelog and the live row * chore(secrets): baseline line numbers after the rebase on #865 and #869 * test(databricks): the schema test's expected rows join their parts with an explicit +
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What
A Databricks-bound expose that states a freshness or completeness SLO now turns on Databricks data quality monitoring (formerly anomaly detection) for the schema the product owns, so Catalog Explorer shows a Quality indicator on its table and every scan lands in
system.data_quality_monitoring.table_results.qos.freshnessSLO,qos.completenessTarget, anobservability.defaultSLIsfreshness/completenessblock, or acontract.dq.rulesentry of typefreshness,completenessoranomaly_detection.contract.dq.monitoring.enabled: falseopts an expose out.databricks_data_quality_monitorper owned schema (object_type = "schema",object_id = databricks_schema.<key>.schema_id),depends_onthe schema's tables so the first scan finds them, with the product's other tables there inanomaly_detection_config.excluded_table_full_names. New modulefluid_build/iac/providers/databricks_quality.py.excluded_table_full_namesis its only setting, and the job parameters that took threshold overrides are closed to new customers since 2025-07-21. So the SLOs choose which tables are watched; their values are not the bounds Databricks checks.fluid applysays so in a notice, andfluid validatewarns about adq.monitoring.windowother than one day.400 RESOURCE_ALREADY_EXISTS). Its import idschema,<schema_id>is a UUID Databricks assigns, soImportBlockgainsid_from: the engine completes the id from state after the schema's own import, and skips it while the schema is not there.import_sectionnever renders such a block.object_idreferencesschema_id). Removing it (opt-out, SLOs dropped, expose removed) deletes its hidden scan job and what it learned, never table data, sodatabricks_data_quality_monitorjoinsRUN_DEFINITION_RESOURCE_TYPES: not gated, printed.tofu applyfails after the tables exist. A new optional plugin hookapply_failure_hints(address, detail)(documented iniac/base.py) adds what it needs and the opt-out to the suggestions (afterCLIError's three generic ones, ahead of thetofu state listhint); the event staysopentofu_apply_failed.Cost (Free Edition)
Scans run on serverless compute billed as
DATA_QUALITY_MONITORING, once when monitoring is turned on and then as often as each table is updated. Free Edition runs the feature; expect the scans to count against its daily compute quota (not measured): past the quota, all compute stops for the rest of the day. The demo's 19 tower products all state SLOs, so their next apply turns on 19 monitors. The CHANGELOG says to opt out what need not be watched. The DBU cost was not measurable in-session:system.billing.usagelagged the scans by hours.Borrowed, not built
databricks_data_quality_monitorresource (present in the pinned 1.140.0; schema dumped from the binary): no REST code, no pin bump.data_quality_monitor(databricks/cli), laktory's generated model, datacontract-cli service levels (no Databricks monitor emission), Elementary (freshness_anomaliesbesidedata_freshness_sla: learned and stated thresholds are complements, the pattern followed here). No repo drives this resource from a data contract.Validation
ruff check fluid_build/ tests/, the isolated dead-code sweep,black==24.10.0 --check, license headers,lint-imports(6 kept), product guardrail,detect-secrets-hookon the changed files: clean.tests/iac/test_iac_databricks_quality.py(37 unit),tests/iac/test_iac_databricks_quality_fake_e2e.py(6, realfluid+tofu+ provider 1.140.0 against the stand-in, which now serves the data-quality endpoints shaped on the live answers),test_iac_tofu_validate.py::test_a_databricks_data_quality_monitor_passes_tofu_validate.-n 6 --dist loadscope --ignore=tests/perf, tofu off PATH,--randomly-seed=20261112): 34824 passed, 9 failed, 2 errors on the tip before the last rebase. Every failure is this machine's shared-venv baseline, identical on main (lazy-core x2, dlt dotenv x4, duckdb sandbox x2 on macOS), plus one known xdist flake (TestSnowflakeLogin, passes alone); the 2 errors arebashlexmissing from the shared venv. After rebasing onto feat(databricks): Unity Catalog primary and foreign keys from the contract #860 and fix(local): no plan-validation failure for an expose an acquisition build lands #862,tests/iac+tests/cliwith real tofu: 6885 passed, 2 failed (the lazy-core baseline).ripwire --quality-delta(branch diff on a pristine base): remaining rows are churn of the touched symbols, test-idiom clones and the stand-in class's size;planned_objects' growth was extracted into_monitor_actions.fluid_iactest_dqm,DROP ... CASCADEafter, 0 left; never touchedworkspaceornorthwind_towers):POST /api/data-quality/v1/monitorson Free Edition: 200, a hidden anomaly-detection workflow, first run SUCCESS after ~8 min.fluid applyof a two-table product with the tower pack's smart-meter SLOs:+14, second plan+0 ~0 -0.system.data_quality_monitoring.table_results:readings→Training(freshness/completenessUnknown,RECENTLY_CREATED); no row for the excludednotes.readings; Quality: Excluded (Reason: User Configured) onnotes.adopted 2 pre-existing resource(s), then an empty plan.~1(provider PATCHesupdate_mask=anomaly_detection_config,data_profiling_configwith an empty config), the list is gone live, next plan empty.-1, printed as not gated, monitor GET404 RESOURCE_DOES_NOT_EXIST.Reviews (/ship)
/security-review: no findings (Opus), re-run after the fix wave: no findings./world-class-inspection(Diff mode): CONCERNS → one P2 (exclusion list shrinking to empty unproven against the real API) resolved by the live measurement plus a stand-in that honoursupdate_maskand a pinning test; the refused-monitor error path got its fix-it. Re-check: all fixed, no new defects./defect-scan(Diff mode): no findings; three LOW UX wording items fixed (Catalog Explorer's "Quality" label, the opt-out named in the notice, a next step in the window warning).Follow-ups
anomaly_detection_confighas nonotification_settings(its only one isdata_profiling_config'son_failure); re-check a newer provider before mappingobservability.onBreach(wording corrected in docs(databricks): correct three claims about data quality monitoring #866).system.billing.usagecatches up.authzlocal indatabricks.py::_refuse_governance.Documentation
docs/providers/databricks.md); its PR is linked here once open.