Values in range

Validations to check that column values, or aggregations of column values, fall within a defined numeric range. All range validations are inclusive by default — use the exclude options to make boundaries strict.

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Column values must be in range

Every row in the column must have a value between the specified minimum and maximum.

Use this when you want to enforce a valid range at the row level — for example, an age column that should always be between 0 and 120, or a discount_percent column that should never exceed 100.

Parameters

Name	Type	Required	Description
Column	Column	✅	The column to validate.
Minimum value	Number	✅	The minimum allowed value.
Exclude minimum value	Boolean	❌	If enabled, values must be strictly greater than the minimum.
Maximum value	Number	✅	The maximum allowed value.
Exclude maximum value	Boolean	❌	If enabled, values must be strictly less than the maximum.

Example

order_id	discount_percent
1	10
2	25
3	110

	Example 1	Example 2
Column	discount_percent	discount_percent
Minimum value	0	0
Exclude minimum value	false	false
Maximum value	100	150
Exclude maximum value	false	false
Result	❌ Fails	✅ Passes
Reason	Row 3 has value 110, which exceeds the maximum of 100	All values are within [0, 150]

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Column values must be greater than

Every row in the column must have a value strictly greater than the specified minimum.

Use this when you want to ensure a column never contains zero or negative values — for example, a price or quantity column.

Parameters

Name	Type	Required	Description
Column	Column	✅	The column to validate.
Minimum value	Number	✅	The value that all column values must exceed.

Example

product_id	price
1	9.99
2	0
3	49.99

	Example 1	Example 2
Column	price	price
Minimum value	0	-1
Result	❌ Fails	✅ Passes
Reason	Row 2 has value 0, which is not strictly greater than 0	All values are greater than -1

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Column values must be greater or equal than

Every row in the column must have a value greater than or equal to the specified minimum.

Use this when you want to allow zero but disallow negative values — for example, a quantity column where 0 is valid but negative numbers are not.

Parameters

Name	Type	Required	Description
Column	Column	✅	The column to validate.
Minimum value	Number	✅	The minimum allowed value (inclusive).

Example

order_id	quantity
1	3
2	0
3	-1

	Example 1	Example 2
Column	quantity	quantity
Minimum value	0	-5
Result	❌ Fails	✅ Passes
Reason	Row 3 has value -1, which is less than 0	All values are greater than or equal to -5

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Column values must be lower than

Every row in the column must have a value strictly less than the specified maximum.

Use this when you want to enforce a strict upper bound — for example, a score column that must always be less than 100 (not equal to it).

Parameters

Name	Type	Required	Description
Column	Column	✅	The column to validate.
Maximum value	Number	✅	The value that all column values must be below.

Example

student_id	score
1	85
2	100
3	72

	Example 1	Example 2
Column	score	score
Maximum value	100	101
Result	❌ Fails	✅ Passes
Reason	Row 2 has value 100, which is not strictly less than 100	All values are less than 101

---

Column values must be lower or equal than

Every row in the column must have a value less than or equal to the specified maximum.

Use this when you want to enforce an inclusive upper bound — for example, a rating column that can go up to 5 but no higher.

Parameters

Name	Type	Required	Description
Column	Column	✅	The column to validate.
Maximum value	Number	✅	The maximum allowed value (inclusive).

Example

review_id	rating
1	4
2	5
3	6

	Example 1	Example 2
Column	rating	rating
Maximum value	5	10
Result	❌ Fails	✅ Passes
Reason	Row 3 has value 6, which exceeds the maximum of 5	All values are less than or equal to 10

---

Column minimum value must be in range

The minimum value found in the column must fall between the specified bounds.

Use this when you want to monitor the lower end of a numeric column without checking individual rows — for example, ensuring the cheapest product in a catalog never drops below a floor price.

Parameters

Name	Type	Required	Description
Column	Column	✅	The column to validate.
Minimum value	Number	✅	The lower bound for the column minimum.
Exclude minimum value	Boolean	❌	If enabled, the column minimum must be strictly greater than this bound.
Maximum value	Number	✅	The upper bound for the column minimum.
Exclude maximum value	Boolean	❌	If enabled, the column minimum must be strictly less than this bound.

Example

product_id	price
1	9.99
2	24.99
3	49.99

The minimum value in price is 9.99.

	Example 1	Example 2
Column	price	price
Minimum value	10	5
Exclude minimum value	false	false
Maximum value	50	50
Exclude maximum value	false	false
Result	❌ Fails	✅ Passes
Reason	Column minimum is 9.99, which is below the allowed bound of 10	Column minimum 9.99 is within [5, 50]

---

Column maximum value must be in range

The maximum value found in the column must fall between the specified bounds.

Use this when you want to monitor the upper end of a numeric column without checking individual rows — for example, ensuring the largest transaction in a batch never exceeds an expected ceiling.

Parameters

Name	Type	Required	Description
Column	Column	✅	The column to validate.
Minimum value	Number	✅	The lower bound for the column maximum.
Exclude minimum value	Boolean	❌	If enabled, the column maximum must be strictly greater than this bound.
Maximum value	Number	✅	The upper bound for the column maximum.
Exclude maximum value	Boolean	❌	If enabled, the column maximum must be strictly less than this bound.

Example

transaction_id	amount
1	150
2	980
3	12500

The maximum value in amount is 12500.

	Example 1	Example 2
Column	amount	amount
Minimum value	0	0
Exclude minimum value	false	false
Maximum value	10000	15000
Exclude maximum value	false	false
Result	❌ Fails	✅ Passes
Reason	Column maximum is 12500, which exceeds the allowed bound of 10000	Column maximum 12500 is within [0, 15000]

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Column mean value must be in range

The mean (average) of all values in the column must fall between the specified bounds.

Use this when you want to monitor the central tendency of a numeric column over time — for example, ensuring the average order value stays within an expected range, which could indicate pricing or data issues if it drifts.

Parameters

Name	Type	Required	Description
Column	Column	✅	The column to validate.
Minimum value	Number	✅	The lower bound for the column mean.
Exclude minimum value	Boolean	❌	If enabled, the column mean must be strictly greater than this bound.
Maximum value	Number	✅	The upper bound for the column mean.
Exclude maximum value	Boolean	❌	If enabled, the column mean must be strictly less than this bound.

Example

order_id	amount
1	50
2	80
3	200

The mean of amount is 110.

	Example 1	Example 2
Column	amount	amount
Minimum value	0	0
Exclude minimum value	false	false
Maximum value	100	200
Exclude maximum value	false	false
Result	❌ Fails	✅ Passes
Reason	Column mean is 110, which exceeds the allowed bound of 100	Column mean 110 is within [0, 200]

---

Column median value must be in range

The median of all values in the column must fall between the specified bounds.

Use this when you want to monitor the typical value in a numeric column, robust to outliers — for example, ensuring the median delivery time stays within SLA expectations even when some deliveries are very late.

Parameters

Name	Type	Required	Description
Column	Column	✅	The column to validate.
Minimum value	Number	✅	The lower bound for the column median.
Exclude minimum value	Boolean	❌	If enabled, the column median must be strictly greater than this bound.
Maximum value	Number	✅	The upper bound for the column median.
Exclude maximum value	Boolean	❌	If enabled, the column median must be strictly less than this bound.

Example

shipment_id	delivery_days
1	2
2	3
3	4
4	30

The median of delivery_days is 3.5.

	Example 1	Example 2
Column	delivery_days	delivery_days
Minimum value	1	1
Exclude minimum value	false	false
Maximum value	3	5
Exclude maximum value	false	false
Result	❌ Fails	✅ Passes
Reason	Column median is 3.5, which exceeds the allowed bound of 3	Column median 3.5 is within [1, 5]

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Column sum must be in range

The sum of all values in the column must fall between the specified bounds.

Use this when you want to validate totals — for example, ensuring the total revenue in a daily batch falls within an expected range, or that the sum of allocated percentages in a distribution table adds up to a known value.

Parameters

Name	Type	Required	Description
Column	Column	✅	The column to validate.
Minimum value	Number	✅	The lower bound for the column sum.
Maximum value	Number	✅	The upper bound for the column sum.

Example

order_id	amount
1	150
2	320
3	85

The sum of amount is 555.

	Example 1	Example 2
Column	amount	amount
Minimum value	600	500
Maximum value	1000	1000
Result	❌ Fails	✅ Passes
Reason	Column sum is 555, which is below the allowed minimum of 600	Column sum 555 is within [500, 1000]

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Column proportion of unique values must be in range

The proportion of unique values in the column (unique count / total count) must fall between the specified bounds.

Use this when you want to monitor cardinality relative to total rows — for example, ensuring a customer_id column has high uniqueness, or that a category column doesn't have too many or too few distinct values relative to the dataset size.

Parameters

Name	Type	Required	Description
Column	Column	✅	The column to validate.
Minimum value	Number	✅	The minimum proportion of unique values. Must be between 0 and 1.
Exclude minimum value	Boolean	❌	If enabled, the proportion must be strictly greater than the minimum.
Maximum value	Number	✅	The maximum proportion of unique values. Must be between 0 and 1.
Exclude maximum value	Boolean	❌	If enabled, the proportion must be strictly less than the maximum.

Example

order_id	category
1	electronics
2	clothing
3	electronics
4	food

category has 3 unique values out of 4 rows, so the proportion is 0.75.

	Example 1	Example 2
Column	category	category
Minimum value	0.8	0.5
Exclude minimum value	false	false
Maximum value	1	1
Exclude maximum value	false	false
Result	❌ Fails	✅ Passes
Reason	Proportion is 0.75, which is below the allowed minimum of 0.8	Proportion 0.75 is within [0.5, 1]

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Column total unique values must be in range

The total number of distinct values in the column must fall between the specified bounds.

Use this when you want to monitor the absolute cardinality of a column — for example, ensuring a country column always has at least the expected number of countries, or that a lookup table hasn't grown beyond a known size.

Parameters

Name	Type	Required	Description
Column	Column	✅	The column to validate.
Minimum value	Number	✅	The minimum number of unique values allowed.
Maximum value	Number	✅	The maximum number of unique values allowed.

Example

sale_id	country
1	Argentina
2	Brazil
3	Argentina
4	Chile

country has 3 distinct values.

	Example 1	Example 2
Column	country	country
Minimum value	5	2
Maximum value	10	10
Result	❌ Fails	✅ Passes
Reason	Total unique values is 3, which is below the allowed minimum of 5	Total unique values 3 is within [2, 10]

Column values must be lower or equal than current date

Every row in the column must have a date value less than or equal to today's date. Fails if any row contains a future date.

Use this when you want to ensure a date column never contains future values — for example, a birth_date, created_at, or signed_at column that should always be in the past or today.

Parameters

Name	Type	Required	Description
Column	Column	✅	The column to validate. Must contain date or timestamp values.

Example

user_id	birth_date	signed_at
1	1990-05-12	2024-01-15
2	2001-11-30	2025-03-08
3	2031-04-01	2026-04-20

	Example 1	Example 2
Column	birth_date	signed_at
Result	❌ Fails	✅ Passes
Reason	Row 3 has 2031-04-01, which is a future date	All values in signed_at are on or before today's date