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Numeric Min

Check: numeric-min-check

Purpose: Validates that values in numeric columns are not below a defined minimum. A row fails if any selected column contains a value less than min_value.

Use the inclusive parameter to control boundary behavior:

  • inclusive: false (default) — value > min_value
  • inclusive: true — value >= min_value
from sparkdq.checks import NumericMinCheckConfig
from sparkdq.core import Severity

NumericMinCheckConfig(
    check_id="price-not-negative",
    columns=["price", "discount"],
    min_value=0.0,
    inclusive=True,
    severity=Severity.CRITICAL
)

- check: numeric-min-check
  check-id: price-not-negative
  columns:
    - price
    - discount
  min-value: 0.0
  inclusive: true
  severity: critical

Typical Use Cases

  • Ensure monetary amounts, quantities, or measurements are non-negative.
  • Enforce minimum threshold requirements for KPIs or scoring metrics.
  • Detect corrupted or invalid numeric values below the physically meaningful range.

← Row-Level Checks