Numeric Max

Numeric Max#

Check: numeric-max-check

Purpose: Checks whether values in the specified numeric columns are less than a defined maximum value (max_value). A row fails the check if any of the selected columns contains a value greater than the maximum, or equal to it if inclusive=False.

You can control inclusivity with the inclusive parameter:

  • inclusive = False (default): value < max_value

  • inclusive = True: value <= max_value

Python Configuration#

from sparkdq.checks import NumericMaxCheckConfig
from sparkdq.core import Severity

NumericMaxCheckConfig(
    check_id="maximum_allowed_discount",
    columns=["discount"],
    max_value=100.0,
    inclusive=True,
    severity=Severity.CRITICAL
)

Declarative Configuration#

- check: numeric-max-check
  check-id: maximum_allowed_discount
  columns:
    - discount
  max-value: 100.0
  inclusive: true
  severity: critical

Typical Use Cases#

  • ✅ Ensure that prices, quantities, or measurements do not exceed defined maximum limits.

  • ✅ Validate sensor readings against physical upper thresholds.

  • ✅ Check maximum allowed values for KPIs, metrics, or score calculations (e.g., percentages ≤ 100).

  • ✅ Detect outliers or erroneous data entries that exceed expected ranges.

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