Date Max

Date Max#

Check: date-max-check

Purpose: Checks whether values in the specified date columns are less than a defined maximum date. A row fails the check if any of the selected columns contains a date after the configured max_value.

You can control inclusivity using the inclusive parameter:

  • inclusive = False (default): value < max_value

  • inclusive = True: value <= max_value

Python Configuration#

from sparkdq.checks import DateMaxCheckConfig
from sparkdq.core import Severity

DateMaxCheckConfig(
    check_id="maximum_allowed_record_date",
    columns=["record_date"],
    max_value="2023-12-31",
    inclusive=True,
    severity=Severity.CRITICAL
)

Declarative Configuration#

- check: date-max-check
  check-id: maximum_allowed_record_date
  columns:
    - record_date
  max-value: "2023-12-31"
  inclusive: true
  severity: critical

Typical Use Cases#

  • ✅ Ensure that event or transaction dates do not lie in the future.

  • ✅ Validate that timestamps for data entries stay within the expected reporting period.

  • ✅ Check that measurement dates are not beyond a configured cutoff date (e.g., end of fiscal year).

  • ✅ Prevent accidental inclusion of incorrectly future-dated records due to data entry errors.

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