Test Definitions Reference

This page provides a complete reference for all data quality test definitions available in the OpenMetadata Python SDK. Tests are organized into two categories: Table-Level Tests and Column-Level Tests.

Importing Test Definitions

All test definitions are available from the metadata.sdk.data_quality module:

from metadata.sdk.data_quality import (
    # Table tests
    TableRowCountToBeBetween,
    TableColumnCountToBeBetween,
    TableCustomSQLQuery,

    # Column tests
    ColumnValuesToBeNotNull,
    ColumnValuesToBeUnique,
    ColumnValuesToBeBetween,
)

Common Parameters

All test definitions support these optional parameters:

Parameter	Type	Description
`name`	`str`	Unique identifier for the test case
`display_name`	`str`	Human-readable name shown in UI
`description`	`str`	Detailed description of what the test validates

Column tests additionally require:

Parameter	Type	Description
`column`	`str`	Name of the column to test (required)

Table-Level Tests

Table-level tests validate properties of entire tables, such as row counts, column counts, or custom SQL queries.

TableRowCountToBeBetween

Validates that the number of rows in a table falls within a specified range. Parameters:

min_count (int, optional): Minimum acceptable number of rows
max_count (int, optional): Maximum acceptable number of rows

Example:

from metadata.sdk.data_quality import TableRowCountToBeBetween

# Table should have between 1,000 and 100,000 rows
test = TableRowCountToBeBetween(min_count=1000, max_count=100000)

# At least 1,000 rows (no maximum)
test = TableRowCountToBeBetween(min_count=1000)

# At most 50,000 rows (no minimum)
test = TableRowCountToBeBetween(max_count=50000)

Use Cases:

Monitor data volume and detect data loss
Validate expected data growth patterns
Detect unexpected data surges

TableRowCountToEqual

Validates that the table has an exact number of rows. Parameters:

row_count (int, required): Expected number of rows

Example:

from metadata.sdk.data_quality import TableRowCountToEqual

# Table must have exactly 50 rows
test = TableRowCountToEqual(row_count=50)

Use Cases:

Validate fixed-size reference tables
Ensure complete dimension table loads
Verify static lookup tables

TableColumnCountToBeBetween

Validates that the number of columns in a table falls within a specified range. Parameters:

min_count (int, optional): Minimum acceptable number of columns
max_count (int, optional): Maximum acceptable number of columns

Example:

from metadata.sdk.data_quality import TableColumnCountToBeBetween

# Table should have between 5 and 20 columns
test = TableColumnCountToBeBetween(min_count=5, max_count=20)

Use Cases:

Schema validation
Detect unexpected column additions or removals
Monitor schema evolution

TableColumnCountToEqual

Validates that the table has an exact number of columns. Parameters:

column_count (int, required): Expected number of columns

Example:

from metadata.sdk.data_quality import TableColumnCountToEqual

# Table must have exactly 10 columns
test = TableColumnCountToEqual(column_count=10)

Use Cases:

Strict schema validation
Ensure schema stability
Prevent schema drift

TableColumnNameToExist

Validates that a specific column exists in the table schema. Parameters:

column_name (str, required): Name of the column that must exist

Example:

from metadata.sdk.data_quality import TableColumnNameToExist

# Ensure 'customer_id' column exists
test = TableColumnNameToExist(column_name="customer_id")

Use Cases:

Verify required columns are present
Ensure critical columns aren’t dropped
Validate schema migrations

TableColumnToMatchSet

Validates that table columns match an expected set of column names. Parameters:

column_names (list[str], required): List of expected column names
ordered (bool, optional): If True, column order must match exactly (default: False)

Example:

from metadata.sdk.data_quality import TableColumnToMatchSet

# Columns should match this set (any order)
test = TableColumnToMatchSet(
    column_names=["id", "name", "email", "created_at"]
)

# Columns must match in exact order
test = TableColumnToMatchSet(
    column_names=["id", "name", "email"],
    ordered=True
)

Use Cases:

Validate complete schema structure
Ensure schema consistency across environments
Detect unexpected schema changes

TableRowInsertedCountToBeBetween

Validates that the number of rows inserted within a time range is within bounds. Parameters:

min_count (int, optional): Minimum acceptable number of inserted rows
max_count (int, optional): Maximum acceptable number of inserted rows
range_type (str, optional): Time unit (“HOUR”, “DAY”, “WEEK”, “MONTH”) (default: “DAY”)
range_interval (int, optional): Number of time units to look back (default: 1)

Example:

from metadata.sdk.data_quality import TableRowInsertedCountToBeBetween

# 100-1000 rows inserted in the last 24 hours
test = TableRowInsertedCountToBeBetween(
    min_count=100,
    max_count=1000,
    range_type="DAY",
    range_interval=1
)

# At least 50 rows inserted in the last 6 hours
test = TableRowInsertedCountToBeBetween(
    min_count=50,
    range_type="HOUR",
    range_interval=6
)

Use Cases:

Monitor data ingestion rates
Detect ingestion pipeline failures
Validate ETL job completions

TableCustomSQLQuery

Validates data using a custom SQL query expression. Parameters:

sql_expression (str, required): SQL query to execute
strategy (str, optional): “ROWS” (count failing rows) or “COUNT” (expect a count) (default: “ROWS”)

Example:

from metadata.sdk.data_quality import TableCustomSQLQuery

# Find negative prices (returns failing rows)
test = TableCustomSQLQuery(
    sql_expression="SELECT * FROM {table} WHERE price < 0",
    strategy="ROWS"
)

# Count orphaned records
test = TableCustomSQLQuery(
    sql_expression="""
        SELECT COUNT(*) FROM {table} t
        LEFT JOIN parent_table p ON t.parent_id = p.id
        WHERE p.id IS NULL
    """,
    strategy="COUNT"
)

Use Cases:

Implement custom business logic validation
Validate referential integrity
Check complex data relationships

TableDiff

Compares two tables and identifies differences in their data. Parameters:

table2 (str, required): Fully qualified name of the comparison table
key_columns (list[str], optional): Columns to use as join keys
table2_key_columns (list[str], optional): Join key columns from table 2
use_columns (list[str], optional): Specific columns to compare
extra_columns (list[str], optional): Additional columns to include in output
table2_extra_columns (list[str], optional): Additional columns from table 2

Example:

from metadata.sdk.data_quality import TableDiff

# Compare tables using 'id' as key
test = TableDiff(
    table2="Postgres.warehouse.staging.reference_customers",
    key_columns=["id"],
    use_columns=["name", "email", "status"]
)

# Compare with different key columns
test = TableDiff(
    table2="MySQL.prod.db.legacy_users",
    key_columns=["customer_id"],
    table2_key_columns=["user_id"],
    use_columns=["name", "email"]
)

Use Cases:

Validate data migrations
Verify data replication
Compare production vs staging data

Column-Level Tests

Column-level tests validate properties of specific columns.

ColumnValuesToBeNotNull

Validates that a column contains no null or missing values. Parameters:

column (str, required): Name of the column to validate

Example:

from metadata.sdk.data_quality import ColumnValuesToBeNotNull

# Email column must not contain nulls
test = ColumnValuesToBeNotNull(column="email")

Use Cases:

Ensure required fields are populated
Validate data completeness
Enforce NOT NULL constraints

ColumnValuesToBeUnique

Validates that all values in a column are unique with no duplicates. Parameters:

column (str, required): Name of the column to validate

Example:

from metadata.sdk.data_quality import ColumnValuesToBeUnique

# Customer ID must be unique
test = ColumnValuesToBeUnique(column="customer_id")

Use Cases:

Validate primary keys
Ensure unique identifiers
Detect duplicate records

ColumnValuesToBeInSet

Validates that all values in a column belong to a specified set. Parameters:

column (str, required): Name of the column to validate
allowed_values (list[str], required): List of acceptable values

Example:

from metadata.sdk.data_quality import ColumnValuesToBeInSet

# Status must be one of these values
test = ColumnValuesToBeInSet(
    column="status",
    allowed_values=["active", "inactive", "pending"]
)

# Country codes must be valid
test = ColumnValuesToBeInSet(
    column="country_code",
    allowed_values=["US", "UK", "CA", "AU", "DE"]
)

Use Cases:

Validate enum values
Enforce categorical constraints
Validate lookup values

ColumnValuesToBeNotInSet

Validates that column values do not contain any forbidden values. Parameters:

column (str, required): Name of the column to validate
forbidden_values (list[str], required): List of values that must not appear

Example:

from metadata.sdk.data_quality import ColumnValuesToBeNotInSet

# Prevent test email addresses
test = ColumnValuesToBeNotInSet(
    column="email",
    forbidden_values=["[email protected]", "[email protected]", "[email protected]"]
)

Use Cases:

Detect test data in production
Blacklist invalid values
Filter out placeholder values

ColumnValuesToMatchRegex

Validates that column values match a specified regular expression pattern. Parameters:

column (str, required): Name of the column to validate
regex (str, required): Regular expression pattern

Example:

from metadata.sdk.data_quality import ColumnValuesToMatchRegex

# Validate email format
test = ColumnValuesToMatchRegex(
    column="email",
    regex=r"^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$"
)

# Validate phone number format (US)
test = ColumnValuesToMatchRegex(
    column="phone",
    regex=r"^\+?1?\d{10}$"
)

# Validate date format (YYYY-MM-DD)
test = ColumnValuesToMatchRegex(
    column="date_string",
    regex=r"^\d{4}-\d{2}-\d{2}$"
)

Use Cases:

Validate data format consistency
Ensure pattern compliance
Detect malformed data

ColumnValuesToNotMatchRegex

Validates that column values do not match a forbidden regular expression pattern. Parameters:

column (str, required): Name of the column to validate
regex (str, required): Regular expression pattern that values must NOT match

Example:

from metadata.sdk.data_quality import ColumnValuesToNotMatchRegex

# Prevent test emails
test = ColumnValuesToNotMatchRegex(
    column="email",
    regex=r".*@test\.com$"
)

# Detect test data markers
test = ColumnValuesToNotMatchRegex(
    column="name",
    regex=r"^test.*"
)

Use Cases:

Detect test data patterns
Prevent specific formats
Identify security risks

ColumnValuesToBeBetween

Validates that all values in a column fall within a specified numeric range. Parameters:

column (str, required): Name of the column to validate
min_value (float, optional): Minimum acceptable value
max_value (float, optional): Maximum acceptable value

Example:

from metadata.sdk.data_quality import ColumnValuesToBeBetween

# Age should be reasonable
test = ColumnValuesToBeBetween(
    column="age",
    min_value=0,
    max_value=120
)

# Price must be positive
test = ColumnValuesToBeBetween(
    column="price",
    min_value=0.01,
    max_value=999999.99
)

# Percentage values
test = ColumnValuesToBeBetween(
    column="completion_rate",
    min_value=0,
    max_value=100
)

Use Cases:

Validate numeric constraints
Detect outliers
Ensure value ranges

ColumnValueMaxToBeBetween

Validates that the maximum value in a column falls within a specified range. Parameters:

column (str, required): Name of the column to validate
min_value (float, optional): Minimum acceptable maximum value
max_value (float, optional): Maximum acceptable maximum value

Example:

from metadata.sdk.data_quality import ColumnValueMaxToBeBetween

# Max temperature should be reasonable
test = ColumnValueMaxToBeBetween(
    column="temperature",
    min_value=-50,
    max_value=50
)

# Highest score should not exceed 100
test = ColumnValueMaxToBeBetween(
    column="score",
    min_value=90,
    max_value=100
)

Use Cases:

Monitor data ranges
Detect upper outliers
Validate maximum constraints

ColumnValueMinToBeBetween

Validates that the minimum value in a column falls within a specified range. Parameters:

column (str, required): Name of the column to validate
min_value (float, optional): Minimum acceptable minimum value
max_value (float, optional): Maximum acceptable minimum value

Example:

from metadata.sdk.data_quality import ColumnValueMinToBeBetween

# Min temperature validation
test = ColumnValueMinToBeBetween(
    column="temperature",
    min_value=-50,
    max_value=0
)

# Minimum age should be at least 18
test = ColumnValueMinToBeBetween(
    column="age",
    min_value=18,
    max_value=25
)

Use Cases:

Monitor lower bounds
Detect lower outliers
Validate minimum constraints

ColumnValueMeanToBeBetween

Validates that the mean (average) value falls within a specified range. Parameters:

column (str, required): Name of the column to validate
min_value (float, optional): Minimum acceptable mean value
max_value (float, optional): Maximum acceptable mean value

Example:

from metadata.sdk.data_quality import ColumnValueMeanToBeBetween

# Average rating should be between 3 and 4.5
test = ColumnValueMeanToBeBetween(
    column="rating",
    min_value=3.0,
    max_value=4.5
)

# Average response time monitoring
test = ColumnValueMeanToBeBetween(
    column="response_time_ms",
    min_value=100,
    max_value=500
)

Use Cases:

Statistical validation
Detect data drift
Monitor averages

ColumnValueMedianToBeBetween

Validates that the median value falls within a specified range. Parameters:

column (str, required): Name of the column to validate
min_value (float, optional): Minimum acceptable median value
max_value (float, optional): Maximum acceptable median value

Example:

from metadata.sdk.data_quality import ColumnValueMedianToBeBetween

# Median salary range
test = ColumnValueMedianToBeBetween(
    column="salary",
    min_value=50000,
    max_value=75000
)

Use Cases:

Robust central tendency checks
Detect skewed distributions
Monitor typical values

ColumnValueStdDevToBeBetween

Validates that the standard deviation falls within a specified range. Parameters:

column (str, required): Name of the column to validate
min_value (float, optional): Minimum acceptable standard deviation
max_value (float, optional): Maximum acceptable standard deviation

Example:

from metadata.sdk.data_quality import ColumnValueStdDevToBeBetween

# Response time variability
test = ColumnValueStdDevToBeBetween(
    column="response_time",
    min_value=0,
    max_value=100
)

Use Cases:

Detect unexpected variability
Monitor data consistency
Validate distribution stability

ColumnValuesSumToBeBetween

Validates that the sum of all values falls within a specified range. Parameters:

column (str, required): Name of the column to validate
min_value (float, optional): Minimum acceptable sum
max_value (float, optional): Maximum acceptable sum

Example:

from metadata.sdk.data_quality import ColumnValuesSumToBeBetween

# Total revenue validation
test = ColumnValuesSumToBeBetween(
    column="revenue",
    min_value=1000000,
    max_value=5000000
)

Use Cases:

Validate totals
Monitor aggregates
Detect unexpected volumes

ColumnValuesMissingCount

Validates the count of missing or null values. Parameters:

column (str, required): Name of the column to validate
missing_count_value (int, optional): Expected number of missing values
missing_value_match (list[str], optional): Additional strings to treat as missing

Example:

from metadata.sdk.data_quality import ColumnValuesMissingCount

# Expect exactly 100 missing values
test = ColumnValuesMissingCount(
    column="optional_field",
    missing_count_value=100
)

# Treat custom strings as missing
test = ColumnValuesMissingCount(
    column="status",
    missing_value_match=["N/A", "Unknown", ""]
)

Use Cases:

Monitor data completeness
Track missing data patterns
Validate optional fields

ColumnValueLengthsToBeBetween

Validates that string lengths fall within a specified range. Parameters:

column (str, required): Name of the column to validate
min_length (int, optional): Minimum acceptable string length
max_length (int, optional): Maximum acceptable string length

Example:

from metadata.sdk.data_quality import ColumnValueLengthsToBeBetween

# Username length validation
test = ColumnValueLengthsToBeBetween(
    column="username",
    min_length=3,
    max_length=20
)

# Description length constraints
test = ColumnValueLengthsToBeBetween(
    column="description",
    min_length=10,
    max_length=500
)

Use Cases:

Validate string constraints
Prevent truncation
Ensure format compliance

ColumnValuesToBeAtExpectedLocation

Validates that a specific value appears at an expected row position. Parameters:

column (str, required): Name of the column to validate
expected_value (str, required): The exact value expected
row_index (int, optional): Zero-based row position (default: 0)

Example:

from metadata.sdk.data_quality import ColumnValuesToBeAtExpectedLocation

# First row should have ID "1"
test = ColumnValuesToBeAtExpectedLocation(
    column="id",
    expected_value="1",
    row_index=0
)

# Top ranked item
test = ColumnValuesToBeAtExpectedLocation(
    column="rank",
    expected_value="first",
    row_index=0
)

Use Cases:

Validate sorted data
Check ordered results
Verify specific positions

Customizing Tests

All tests support customization through fluent methods:

from metadata.sdk.data_quality import ColumnValuesToBeNotNull

test = ColumnValuesToBeNotNull(column="email") \
    .with_name("email_completeness_check") \
    .with_display_name("Email Completeness Validation") \
    .with_description("Ensures all customer records have email addresses") \
    .with_compute_row_count(True)

Or pass values directly to the constructor:

test = ColumnValuesToBeNotNull(
    column="email",
    name="email_completeness_check",
    display_name="Email Completeness Validation",
    description="Ensures all customer records have email addresses"
)

Next Steps

Learn how to use these tests with TestRunner
Apply tests to DataFrame Validation
Explore Advanced Usage patterns

Overview

Data Quality & Observability

Test Definitions Reference

Test Definitions Reference

Importing Test Definitions

Common Parameters

Table-Level Tests

TableRowCountToBeBetween

TableRowCountToEqual

TableColumnCountToBeBetween

TableColumnCountToEqual

TableColumnNameToExist

TableColumnToMatchSet

TableRowInsertedCountToBeBetween

TableCustomSQLQuery

TableDiff

Column-Level Tests

ColumnValuesToBeNotNull

ColumnValuesToBeUnique

ColumnValuesToBeInSet

ColumnValuesToBeNotInSet

ColumnValuesToMatchRegex

ColumnValuesToNotMatchRegex

ColumnValuesToBeBetween

ColumnValueMaxToBeBetween

ColumnValueMinToBeBetween

ColumnValueMeanToBeBetween

ColumnValueMedianToBeBetween

ColumnValueStdDevToBeBetween

ColumnValuesSumToBeBetween

ColumnValuesMissingCount

ColumnValueLengthsToBeBetween

ColumnValuesToBeAtExpectedLocation

Customizing Tests

Next Steps

Overview

Data Quality & Observability

​Test Definitions Reference

​Importing Test Definitions

​Common Parameters

​Table-Level Tests

​TableRowCountToBeBetween

​TableRowCountToEqual

​TableColumnCountToBeBetween

​TableColumnCountToEqual

​TableColumnNameToExist

​TableColumnToMatchSet

​TableRowInsertedCountToBeBetween

​TableCustomSQLQuery

​TableDiff

​Column-Level Tests

​ColumnValuesToBeNotNull

​ColumnValuesToBeUnique

​ColumnValuesToBeInSet

​ColumnValuesToBeNotInSet

​ColumnValuesToMatchRegex

​ColumnValuesToNotMatchRegex

​ColumnValuesToBeBetween

​ColumnValueMaxToBeBetween

​ColumnValueMinToBeBetween

​ColumnValueMeanToBeBetween

​ColumnValueMedianToBeBetween

​ColumnValueStdDevToBeBetween

​ColumnValuesSumToBeBetween

​ColumnValuesMissingCount

​ColumnValueLengthsToBeBetween

​ColumnValuesToBeAtExpectedLocation

​Customizing Tests

​Next Steps

Test Definitions Reference

Importing Test Definitions

Common Parameters

Table-Level Tests

TableRowCountToBeBetween

TableRowCountToEqual

TableColumnCountToBeBetween

TableColumnCountToEqual

TableColumnNameToExist

TableColumnToMatchSet

TableRowInsertedCountToBeBetween

TableCustomSQLQuery

TableDiff

Column-Level Tests

ColumnValuesToBeNotNull

ColumnValuesToBeUnique

ColumnValuesToBeInSet

ColumnValuesToBeNotInSet

ColumnValuesToMatchRegex

ColumnValuesToNotMatchRegex

ColumnValuesToBeBetween

ColumnValueMaxToBeBetween

ColumnValueMinToBeBetween

ColumnValueMeanToBeBetween

ColumnValueMedianToBeBetween

ColumnValueStdDevToBeBetween

ColumnValuesSumToBeBetween

ColumnValuesMissingCount

ColumnValueLengthsToBeBetween

ColumnValuesToBeAtExpectedLocation

Customizing Tests

Next Steps