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

For column-level tests (null checks, uniqueness, regex, ranges, and more), see Column-Level Test Definitions.

​Test Definitions Reference

​Importing Test Definitions

​Common Parameters

​Table-Level Tests

​TableRowCountToBeBetween

​TableRowCountToEqual

​TableColumnCountToBeBetween

​TableColumnCountToEqual

​TableColumnNameToExist

​TableColumnToMatchSet

​TableRowInsertedCountToBeBetween

​TableCustomSQLQuery

​TableDiff

Test Definitions Reference

Importing Test Definitions

Common Parameters

Table-Level Tests

TableRowCountToBeBetween

TableRowCountToEqual

TableColumnCountToBeBetween

TableColumnCountToEqual

TableColumnNameToExist

TableColumnToMatchSet

TableRowInsertedCountToBeBetween

TableCustomSQLQuery

TableDiff