feat(data-analytics-demo): T-10 MetricFlow semantic layer#90
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Phase 6 of the data-analytics-demo bolt-on. Ships the single source of truth for KPI definitions and a validator that enforces the MetricFlow schema invariants the rest of the package depends on. What lands: - semantic/kpi.yml — 3 semantic models (customers, subscriptions, invoices) and 4 metrics (customers, active_subscriptions, monthly_recurring_revenue, paid_invoice_volume). Each metric carries ≥ 1 dimension and ≥ 1 measure per AC-6.2. - src/data_analytics_demo/semantic/validator.py — pure-Python validator. Parses the YAML, walks semantic_models[] / metrics[], and enforces: required keys, non-empty dims + measures, metric.measure references a declared measure, metric.dimensions all reference declared dimensions. - tests/test_semantic.py — 5 cases covering AC-6.1 + AC-6.2 + negative paths (missing file, empty dim list, unknown measure ref) + canonical metric inventory. - Makefile semantic-validate target + cli.py semantic subcommand wired up. - types-PyYAML added to dev deps so mypy --strict sees the yaml stubs. Local verify: - `make semantic-validate` → "OK — 3 semantic models / 4 metrics: …" - ruff OK / mypy OK (21 source files) / pytest 32 PASS / coverage 87.20% - check-doc-drift / check-adr-claims unchanged (0 fail / 77/77) Design note: the validator is independent of the MetricFlow CLI by design — it enforces the structural invariants the package relies on without shelling out to `mf validate-configs`, so the test suite has no CLI dependency and runs cleanly under pytest.
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Phase 6 of the data-analytics-demo bolt-on. Ships the single source of truth for KPI definitions and a validator that enforces the MetricFlow schema invariants the rest of the package depends on. What lands: - semantic/kpi.yml — 3 semantic models (customers, subscriptions, invoices) and 4 metrics (customers, active_subscriptions, monthly_recurring_revenue, paid_invoice_volume). Each metric carries ≥ 1 dimension and ≥ 1 measure per AC-6.2. - src/data_analytics_demo/semantic/validator.py — pure-Python validator. Parses the YAML, walks semantic_models[] / metrics[], and enforces: required keys, non-empty dims + measures, metric.measure references a declared measure, metric.dimensions all reference declared dimensions. - tests/test_semantic.py — 5 cases covering AC-6.1 + AC-6.2 + negative paths (missing file, empty dim list, unknown measure ref) + canonical metric inventory. - Makefile semantic-validate target + cli.py semantic subcommand wired up. - types-PyYAML added to dev deps so mypy --strict sees the yaml stubs. Local verify: - `make semantic-validate` → "OK — 3 semantic models / 4 metrics: …" - ruff OK / mypy OK (21 source files) / pytest 32 PASS / coverage 87.20% - check-doc-drift / check-adr-claims unchanged (0 fail / 77/77) Design note: the validator is independent of the MetricFlow CLI by design — it enforces the structural invariants the package relies on without shelling out to `mf validate-configs`, so the test suite has no CLI dependency and runs cleanly under pytest. Co-authored-by: leagames0221-sys <leagames0221@users.noreply.github.com>
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Summary
Phase 6 of the data-analytics-demo bolt-on. Ships the single source of truth for KPI definitions (MetricFlow-compatible YAML) and a pure-Python validator that enforces the schema invariants the rest of the package depends on.
What lands
semantic/kpi.ymlsrc/data_analytics_demo/semantic/validator.pytests/test_semantic.pysemantic-validate+cli.py semanticpyproject.tomltypes-PyYAMLto dev deps so mypy--strictsees yaml stubsAC coverage
make semantic-validateexits 0 on a valid filetest_ac_6_1_valid_file_returns_report+ live make-target runvalidator._validate_metric;test_ac_6_2_…plus the negativetest_empty_metric_dimensions_raisestestWhy an in-process validator instead of
mf validate-configsThe MetricFlow CLI is a heavy entry point and ties the test suite to a shell environment + dbt project context. The structural invariants this package relies on (existence of metrics + their measure/dimension cross-refs) are a small subset of MetricFlow's full validation surface. Putting that subset in
validator.pykeeps the test suite self-contained and gives us a clear, AC-traceable failure mode when the YAML drifts.Local verify
make semantic-validate→OK — 3 semantic models / 4 metrics: customers, active_subscriptions, monthly_recurring_revenue, paid_invoice_volumeruff+mypy --strictclean (21 source files)pytest32 passed; coverage 87.20%check-doc-drift.mjs0 fail /check-adr-claims.mjs77/77Test plan