Skip to main content
Tests define data quality checks for your table assets. Create tests to monitor data quality metrics and detect issues.

​
Test Object

from elementary_python_sdk.core.types.test import Test, TestSeverity

test = Test(
    id="string",                    # Required: Unique identifier
    name="string",                  # Required: Test name
    test_type="string",            # Required: Type of test
    asset_id="string",              # Required: ID of the table asset
    severity=TestSeverity.ERROR,    # Required: Test severity
    description="string",           # Optional: Test description
    column_name="string",           # Optional: Column name (for column-level tests)
    config={},                      # Optional: Test configuration
    meta={}                         # Optional: Additional metadata
)

​
Required Fields

FieldTypeDescription
idstringUnique identifier for the test
namestringDisplay name for the test
test_typestringType of test (e.g., "custom", "row_count", "null_rate", "uniqueness")
asset_idstringID of the table asset this test is associated with (must match a TableAsset id)
severityTestSeveritySeverity level: TestSeverity.ERROR or TestSeverity.WARNING

​
Optional Fields

FieldTypeDescription
descriptionstringHuman-readable description of what the test checks
column_namestringColumn name for column-level tests (leave None for table-level tests)
configdictTest-specific configuration parameters
metadictAdditional metadata for the test

​
Test Severity

Tests can have two severity levels:
  • TestSeverity.ERROR - Critical issues that should be addressed immediately
  • TestSeverity.WARNING - Non-critical issues that should be monitored

​
Test Types

Common test types include:
  • "custom" - Custom test logic
  • "row_count" - Check row count
  • "null_rate" - Check null percentage
  • "uniqueness" - Check for duplicate values
  • "freshness" - Check data freshness
  • "volume_anomaly" - Detect volume anomalies
  • "freshness_anomaly" - Detect freshness anomalies

​
Example

from elementary_python_sdk.core.types.test import Test, TestSeverity

# Table-level test
table_test = Test(
    id="users_row_count_test",
    name="Users table row count check",
    test_type="row_count",
    asset_id="analytics.public.users",
    severity=TestSeverity.ERROR,
    description="Ensures the users table has at least 1000 rows",
    config={"min_rows": 1000}
)

# Column-level test
column_test = Test(
    id="users_email_uniqueness_test",
    name="Email uniqueness check",
    test_type="uniqueness",
    asset_id="analytics.public.users",
    severity=TestSeverity.ERROR,
    description="Ensures email addresses are unique",
    column_name="email",
    config={}
)

​
Best Practices

  1. Use descriptive names - Choose clear, descriptive test names that explain what the test checks
  2. Link to assets - Always ensure the asset_id matches an existing table asset ID
  3. Set appropriate severity - Use ERROR for critical checks and WARNING for monitoring
  4. Include descriptions - Add descriptions to help your team understand test purpose
  5. Use column_name for column tests - Set column_name for column-level tests, leave None for table-level tests
Tests are replaced on each ingest, so include all current test definitions in every request.