Date Min

Date Min#

Check: date-min-check

Purpose: Checks whether values in the specified date columns are greater than a defined minimum date. A row fails the check if any of the selected columns contains a date before the configured min_value.

You can control inclusivity using the inclusive parameter:

  • inclusive = False (default): value > min_value

  • inclusive = True: value >= min_value

Python Configuration#

from sparkdq.checks import DateMinCheckConfig
from sparkdq.core import Severity

DateMinCheckConfig(
    check_id="minimum_allowed_record_date",
    columns=["record_date"],
    min_value="2020-01-01",
    inclusive=True,
    severity=Severity.CRITICAL
)

Declarative Configuration#

- check: date-min-check
  check-id: minimum_allowed_record_date
  columns:
    - record_date
  min-value: "2020-01-01"
  inclusive: true
  severity: critical

Typical Use Cases#

  • ✅ Ensure that event or transaction dates are not earlier than a defined start date.

  • ✅ Check that data entries only contain dates within the valid business period.

  • ✅ Prevent processing of outdated or legacy data outside of the expected time range.

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