hollandkevint Testing Pyramid for Data
Run tests in this order. Cheapest and fastest first:
| Layer | What It Catches | Examples |
|---|---|---|
| Schema tests (run first) | Structural failures | Column types, not-null, uniqueness, accepted values |
| Business rule tests | Logic errors | Cross-field validation, referential integrity, range checks |
| Integration tests (run last) | System-level drift | Cross-system reconciliation, end-to-end row counts |
Schema tests are cheap. Run them on every pipeline execution. Business rule tests are mid-tier — run them on staging and production. Integration tests are expensive — run them on a schedule (daily or pre-release).
dbt Test Patterns
Generic tests for reusable checks. Apply across models:
models:
- name: fct_encounters
columns:
- name: encounter_id
tests: [not_null, unique]
- name: encounter_type
tests:
- accepted_values:
values: ['inpatient', 'outpatient', 'emergency', 'observation']
- name: patient_id
tests:
- relationships:
to: ref('dim_patient')
field: patient_id
Custom generic test for row count tolerance:
{% test row_count_within_tolerance(model, min_count, max_count) %}
select count(*) as row_count
from {{ model }}
having count(*) < {{ min_count }} or count(*) > {{ max_count }}
{% endtest %}
Singular tests for business logic specific to one model. Use singular tests when the logic doesn't generalize.
Data Contracts
A data contract is a product spec for your data. It defines what consumers can depend on.
contract:
name: fct_encounters
version: 2
owner: data-platform-team
sla:
freshness: "< 4 hours from source update"
completeness: ">= 99.5% of expected rows"
accuracy: ">= 99.9% match to source of record"
schema:
encounter_id: {type: bigint, nullable: false, unique: true}
patient_id: {type: bigint, nullable: false}
encounter_date: {type: date, nullable: false}
Producer responsibilities: Meet the SLA, notify consumers before breaking changes, version the schema.
Consumer expectations: Query only contracted fields, respect the grain, report quality issues.
Circuit Breakers
Wire quality gates into pipeline stages. When a check fails, block the pipeline and alert.
- Schema violation: Block immediately. Bad schema corrupts everything downstream.
- Row count outside tolerance: Block and alert. Investigate before proceeding.
- Freshness SLA breach: Alert the on-call. Don't block unless downstream consumers can't tolerate stale data.
- Business rule failure: Block if the failure rate exceeds threshold (e.g., >1% of rows). Alert if below.
Cross-reference data-quality-assessment for the 4-stage quality model (detect → assess → respond → prevent).
CRITICAL: Never auto-heal data quality issues in production. Alert, block, investigate. Auto-fixes mask root causes and erode trust faster than stale data.
Monitoring
Track quality over time, not just point-in-time pass/fail:
- Row count trends: Expected vs actual with tolerance bands. A sudden 40% drop is a pipeline failure. A gradual 5% weekly decline is a source issue.
- Freshness: Time since last successful pipeline run. Track P50 and P95, not just "last run."
- Anomaly detection: Statistical thresholds beat static thresholds. A metric that normally ranges 1,000-1,200 firing at 950 is more useful than a static "alert below 500" that never triggers.
Cross-References
For quality scoring methodology (the 5-dimension rubric and maturity model), see data-quality-assessment. This skill covers how to automate those checks in your pipeline.