Defines a dbt test taxonomy and volume anomaly detection for data quality; use when adding tests to dbt models, implementing data validation, or investigating data quality incidents.
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---
name: quality-checks
description: "Defines a dbt test taxonomy and volume anomaly detection for data quality; use when adding tests to dbt models, implementing data validation, or investigating data quality incidents."
---
# Skill: Data Quality Checks
## When to load
When adding tests to dbt models, implementing data validation, or investigating quality incidents.
## dbt Test Taxonomy
```yaml
models:
- name: fct_orders
columns:
- name: order_key
tests: [unique, not_null]
- name: user_key
tests:
- not_null
- relationships:
to: ref('dim_users')
field: user_key
- name: total_amount
tests:
- not_null
- dbt_utils.expression_is_true:
expression: ">= 0"
- name: order_status
tests:
- accepted_values:
values: ['pending', 'processing', 'completed', 'cancelled']
tests:
- dbt_utils.recency:
datepart: hour
field: loaded_at
interval: 4
```
## Volume Anomaly Detection
```sql
-- Alert when daily row count deviates > 3σ from 30-day rolling average
WITH stats AS (
SELECT
load_date, row_count,
AVG(row_count) OVER (ORDER BY load_date ROWS BETWEEN 29 PRECEDING AND 1 PRECEDING) AS rolling_avg,
STDDEV(row_count) OVER (ORDER BY load_date ROWS BETWEEN 29 PRECEDING AND 1 PRECEDING) AS rolling_std
FROM daily_counts
)
SELECT *, CASE WHEN ABS(row_count - rolling_avg) > 3 * rolling_std THEN 'ANOMALY' ELSE 'OK' END AS status
FROM stats WHERE load_date = CURRENT_DATE - 1;
```
