Favorita: dbt Data Tests
Four singular dbt tests that catch the failures that actually break forecasts: broken date continuity, train/test leakage, row-count drift, and sales that don't reconcile. The assertions that let you trust a pipeline you didn't watch run.
Primary Outcome
A set of data-integrity tests that fail the build before bad data reaches a model or dashboard.
Solution
Four singular dbt tests that encode the things that must be true about the data and fail the build the moment they aren't.
Deliverables
assert_date_spine_continuous— no gaps in the calendarassert_train_test_date_boundary— no leakage across the splitassert_sales_fct_row_count— row counts stay in expected rangeassert_company_sales_reconcile— aggregates reconcile to source
Strategic Context
Generic tests check structure; they don't know your domain. The tests that matter encode the specific things that must be true about your data — no leakage, no gaps, reconciled totals. Writing them down is how data quality becomes a guarantee instead of a hope.
Technical Architecture
Generic tests check structure; they don't know your domain. The tests that matter encode the specific things that must be true about your data — no leakage, no gaps, reconciled totals. Writing them down is how data quality becomes a guarantee instead of a hope.
Problem Statement
Schema tests pass while domain-specific failures — date gaps, train/test leakage, unreconciled totals — slip through and quietly corrupt forecasts.
Links
What's Included
The dbt/tests directory: four singular SQL tests covering date continuity, train/test boundaries, row-count bounds, and sales reconciliation.
FAQs
How are these different from schema tests?
Schema tests check structure (unique, not_null). These check domain truths — leakage, continuity, reconciliation — written as SQL.
Do they slow the build much?
Marginally. The cost is trivial next to shipping a forecast built on broken data.
Tech Stack
Tool 1
Tool 4
Tool 4
Tool 3
Tool 2
Tool 4
Primary Outcome
A set of data-integrity tests that fail the build before bad data reaches a model or dashboard.
Problem Statement
Schema tests pass while domain-specific failures — date gaps, train/test leakage, unreconciled totals — slip through and quietly corrupt forecasts.
Solution
Four singular dbt tests that encode the things that must be true about the data and fail the build the moment they aren't.
Links
Deliverables
assert_date_spine_continuous— no gaps in the calendarassert_train_test_date_boundary— no leakage across the splitassert_sales_fct_row_count— row counts stay in expected rangeassert_company_sales_reconcile— aggregates reconcile to source
What's Included
The dbt/tests directory: four singular SQL tests covering date continuity, train/test boundaries, row-count bounds, and sales reconciliation.
Strategic Context
Generic tests check structure; they don't know your domain. The tests that matter encode the specific things that must be true about your data — no leakage, no gaps, reconciled totals. Writing them down is how data quality becomes a guarantee instead of a hope.
FAQs
How are these different from schema tests?
Schema tests check structure (unique, not_null). These check domain truths — leakage, continuity, reconciliation — written as SQL.
Do they slow the build much?
Marginally. The cost is trivial next to shipping a forecast built on broken data.
Technical Architecture
Generic tests check structure; they don't know your domain. The tests that matter encode the specific things that must be true about your data — no leakage, no gaps, reconciled totals. Writing them down is how data quality becomes a guarantee instead of a hope.
Tech Stack
Tool 1
Tool 4
Tool 4
Tool 3
Tool 2
Tool 4