How it Works

The expect_column_min_to_be_between test from the dbt-expectations package validates that the minimum value in a column falls within a specified range. It's the counterpart to expect_column_max_to_be_between and is useful for catching data that unexpectedly dips below an acceptable floor — for example, negative prices, zero-value IDs, or dates in the distant past.

Configuration options include group_by, row_condition, and strictly for exclusive boundary comparisons.

Steps and Conditions

1. Column Selection: Choose the column whose minimum should be validated.

2. Define Range: Set min_value and/or max_value to bound the acceptable minimum.

3. Optional Config: Apply group_by, row_condition, and strictly as needed.

4. Execution: The column minimum is computed and compared against the range.

5. Outcome: Pass if the minimum is within range; fail if it falls outside.

Example Usage: Energy Sector

An energy company monitors meter readings and wants to ensure the smallest recorded reading is never negative (which would indicate a sensor fault).

models:
  - name: meter_readings
    columns:
      - name: kwh_consumed
        tests:
          - dbt_expectations.expect_column_min_to_be_between:
              min_value: 0
              row_condition: "meter_status = 'active'"

models:
  - name: meter_readings
    columns:
      - name: kwh_consumed
        tests:
          - dbt_expectations.expect_column_min_to_be_between:
              min_value: 0
              row_condition: "meter_status = 'active'"

models:
  - name: meter_readings
    columns:
      - name: kwh_consumed
        tests:
          - dbt_expectations.expect_column_min_to_be_between:
              min_value: 0
              row_condition: "meter_status = 'active'"

Negative readings from active meters trigger a failure, flagging potential sensor errors for investigation.