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Date Min

Check: date-min-check

Purpose: Validates that values in date columns are not earlier than a defined minimum date. A row fails if any selected column contains a date before 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 DateMinCheckConfig
from sparkdq.core import Severity

DateMinCheckConfig(
    check_id="record-date-not-before-start",
    columns=["record_date"],
    min_value="2020-01-01",
    inclusive=True,
    severity=Severity.CRITICAL
)

- check: date-min-check
  check-id: record-date-not-before-start
  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 the defined operational start date.
  • Reject legacy or historically stale records outside the valid business period.
  • Validate that data entries are aligned with a system go-live or migration cutover date.

← Row-Level Checks