DataFrame Validation

The DataFrameValidator class lets you validate pandas DataFrames directly within your extract, transform, load (ETL) workflows, before data reaches its destination. This lets you catch data quality issues early, preventing bad data from contaminating your data warehouse or analytics systems.

Overview

DataFrame validation is ideal for:

Validating transformed data before loading to destinations.
Processing large datasets in chunks with memory efficiency.
Short-circuiting ETL pipelines on validation failures.
Providing immediate feedback during data transformations.
Publishing validation results back to Collate.

Basic Usage

The following sections walk through creating a validator, adding tests, and running validation against a DataFrame.

Creating a Validator

Create a DataFrameValidator instance and configure the SDK before adding tests:

from metadata.sdk import configure
from metadata.sdk.data_quality.dataframes import DataFrameValidator

# Configure SDK
configure(
    host="http://localhost:8585/api",
    jwt_token="your-jwt-token"
)

# Create validator
validator = DataFrameValidator()

Adding Tests

Add test definitions to validate your DataFrame:

from metadata.sdk.data_quality import (
    ColumnValuesToBeNotNull,
    ColumnValuesToBeUnique,
    ColumnValuesToBeBetween
)

# Add column-level tests
validator.add_test(
    ColumnValuesToBeNotNull(column="email")
)

validator.add_test(
    ColumnValuesToBeUnique(column="customer_id")
)

validator.add_test(
    ColumnValuesToBeBetween(column="age", min_value=18, max_value=120)
)

Validating a DataFrame

Call validator.validate(df) to run all configured tests against the DataFrame:

import pandas as pd

# Load or create your DataFrame
df = pd.read_csv("customers.csv")

# Validate the DataFrame
result = validator.validate(df)

# Check if validation passed
if result.success:
    print("✓ Validation passed - safe to load data")
    load_to_warehouse(df)
else:
    print("✗ Validation failed")
    for test_case, test_result in result.test_cases_and_results:
        if test_result.testCaseStatus != "Success":
            print(f"  - {test_case.name.root}: {test_result.result}")

Complete ETL Example

Here’s a complete example of validating transformed data in an ETL pipeline:

import pandas as pd
from sqlalchemy import create_engine
from metadata.sdk import configure
from metadata.sdk.data_quality.dataframes import DataFrameValidator
from metadata.sdk.data_quality import (
    ColumnValuesToBeNotNull,
    ColumnValuesToBeUnique,
    ColumnValuesToBeBetween,
    ColumnValuesToMatchRegex
)

# Configure SDK
configure(
    host="http://localhost:8585/api",
    jwt_token="your-jwt-token"
)

# Extract: Read source data
df = pd.read_csv("raw_customers.csv")

# Transform: Clean and enrich data
df["email"] = df["email"].str.lower().str.strip()
df["created_at"] = pd.to_datetime(df["created_at"])
df = df.dropna(subset=["customer_id"])

# Validate: Create and configure validator
validator = DataFrameValidator()
validator.add_tests(
    ColumnValuesToBeNotNull(column="customer_id"),
    ColumnValuesToBeNotNull(column="email"),
    ColumnValuesToBeUnique(column="customer_id"),
    ColumnValuesToBeBetween(column="age", min_value=0, max_value=120),
    ColumnValuesToMatchRegex(
        column="email",
        regex=r"^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$"
    )
)

# Run validation
result = validator.validate(df)

# Load: Only load if validation passes
if result.success:
    engine = create_engine("postgresql://user:pass@localhost/warehouse")
    df.to_sql("customers", engine, if_exists="replace", index=False)
    print(f"✓ Loaded {len(df)} records successfully")

else:
    print("✗ Data quality validation failed. Data not loaded.")


# Optionally publish results to Collate
result.publish("Postgres.warehouse.public.customers")

Using Tests from Collate

Instead of defining tests in code, load tests that are configured in Collate:

from metadata.sdk.data_quality.dataframes import DataFrameValidator

# Create validator
validator = DataFrameValidator()

# Load all tests defined in Collate for a specific table
validator.add_openmetadata_table_tests("BigQuery.analytics.staging.customers")

# Validate DataFrame against those tests
result = validator.validate(df)

if result.success:
    load_to_destination(df)

# Optionally publish results to Collate, recommended so all data stakeholders are up to date
result.publish("Postgres.warehouse.public.customers")

This approach enables:

Separation of concerns: Data stewards define quality criteria in the UI; engineers execute in code.
Dynamic test updates: Test criteria changes don’t require code deployments.
Consistency: Same tests used for table validation and DataFrame validation.

Chunk-Based Validation

For large datasets that don’t fit in memory, validate data in chunks:

Method 1: Manual Chunk Validation

Iterate through chunks manually, validating and loading each one, then merge results at the end:

import pandas as pd
from metadata.sdk.data_quality.dataframes import DataFrameValidator, ValidationResult

validator = DataFrameValidator()
validator.add_openmetadata_table_tests("Postgres.warehouse.staging.transactions")

results = []

# Process file in chunks
for chunk in pd.read_csv("large_file.csv", chunksize=10000):
    # Validate chunk
    result = validator.validate(chunk)
    results.append(result)

    # Load if valid, otherwise stop
    if result.success:
        load_chunk_to_database(chunk)
    else:
        print(f"Validation failed on chunk")
        rollback_all_chunks()
        break

# Merge all results
final_result = ValidationResult.merge(*results)

# Publish aggregated results
final_result.publish("Postgres.warehouse.staging.transactions")

Method 2: Using the `run()` Method

The run() method provides a cleaner approach with automatic chunk handling:

from metadata.sdk.data_quality.dataframes import DataFrameValidator
import pandas as pd
from sqlalchemy import create_engine, Table, MetaData, insert, delete

# Configure validator
validator = DataFrameValidator()
validator.add_openmetadata_table_tests("Postgres.warehouse.staging.orders")

# Setup database connection
engine = create_engine("postgresql://user:pass@localhost/warehouse")

# Define success and failure handlers
def load_chunk(df, validation_result):
    """Load validated chunk to database"""
    with engine.connect() as conn:
        table = Table("orders", MetaData(), autoload_with=conn)
        conn.execute(insert(table).values(), df.to_dict(orient="records"))

def handle_failure(df, validation_result):
    """Rollback on validation failure"""
    with engine.connect() as conn:
        table = Table("orders", MetaData(), autoload_with=conn)
        conn.execute(delete(table))
    print(f"Validation failed: {validation_result}")

# Run validation on chunks
result = validator.run(
    pd.read_csv("orders.csv", chunksize=5000),
    on_success=load_chunk,
    on_failure=handle_failure
)

# Publish results
if result.success:
    print("✓ All chunks validated and loaded successfully")
else:
    print("✗ Validation failed - transaction rolled back")

result.publish("Postgres.warehouse.staging.orders")

Transaction-Safe Chunk Processing

Use a context manager to ensure atomic transactions:

from metadata.sdk.data_quality.dataframes import DataFrameValidator
import pandas as pd
from sqlalchemy import create_engine, MetaData, Table, insert

class DatabaseSession:
    def __init__(self, connection_string, table_name):
        self.engine = create_engine(connection_string)
        self.table_name = table_name
        self._conn = None

    def __enter__(self):
        self._conn = self.engine.connect()
        self._trans = self._conn.begin()
        self.table = Table(self.table_name, MetaData(), autoload_with=self._conn)
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        if exc_type is not None:
            self._trans.rollback()
        else:
            self._trans.commit()
        self._conn.close()

    def load_chunk(self, df, validation_result):
        """Load chunk to database"""
        self._conn.execute(
            insert(self.table).values(),
            df.to_dict(orient="records")
        )

    def rollback(self, df, validation_result):
        """Rollback transaction"""
        self._trans.rollback()

# Use in validation workflow
validator = DataFrameValidator()
validator.add_openmetadata_table_tests("Postgres.warehouse.staging.sales")

with DatabaseSession("postgresql://user:pass@localhost/warehouse", "sales") as session:
    result = validator.run(
        pd.read_csv("sales_data.csv", chunksize=10000),
        on_success=session.load_chunk,
        on_failure=session.rollback
    )

print(f"Validation {'succeeded' if result.success else 'failed'}")

Failure Modes

As of version 1.11.0.0 of the SDK, DataFrameValidator supports only one failure mode: short circuit.

from metadata.sdk.data_quality.dataframes import FailureMode

# SHORT_CIRCUIT (default): Stop on first test failure
result = validator.validate(df, mode=FailureMode.SHORT_CIRCUIT)

# In chunk-based processing, SHORT_CIRCUIT stops processing additional chunks
result = validator.run(
    data_chunks,
    on_success=load_chunk,
    on_failure=rollback,
    mode=FailureMode.SHORT_CIRCUIT  # Stop on first failed chunk
)

Future versions will include additional modes to report back failing rows or skipping failing batches.

Working with Validation Results

Use the result object returned by validate() or run() to inspect test outcomes, merge chunk results, and publish to Collate.

Accessing Test Results

Iterate through individual test results to inspect each test’s outcome:

result = validator.validate(df)

# Check overall success
if result.success:
    print("All tests passed")

# Iterate through individual test results
for test_case, test_result in result.test_cases_and_results:
    print(f"Test: {test_case.name.root}")
    print(f"Status: {test_result.testCaseStatus}")
    print(f"Details: {test_result.result}")

Merging Results from Multiple Chunks

Use ValidationResult.merge() to combine results from all chunks into a single aggregated result:

from metadata.sdk.data_quality.dataframes import ValidationResult

results = []

for chunk in data_chunks:
    result = validator.validate(chunk)
    results.append(result)

# Merge all chunk results
merged_result = ValidationResult.merge(*results)

# Merged result contains aggregated information
print(f"Overall success: {merged_result.success}")

Publishing Results to Collate

After validation, publish results back to Collate for tracking and alerting:

result = validator.validate(df)

# Publish results to Collate
result.publish("Postgres.warehouse.staging.customer_data")

This enables:

Historical tracking of data quality trends.
Alerting on validation failures.
Visualization in Collate UI.
Centralized data quality reporting.

Important Considerations for Chunk-Based Validation

When using chunk-based validation, be aware of tests that require the full dataset:

Tests That Require Full Table

Some tests analyze the entire dataset and may produce incorrect results when run on chunks:

TableRowCountToBeBetween: Counts rows in each chunk, not the full dataset.
TableRowCountToEqual: Validates chunk size, not full dataset size.
ColumnValuesSumToBeBetween: Sums values per chunk, not across all data.

The SDK will issue a warning when such tests are detected:

WholeTableTestsWarning: Running tests that require the whole table on chunks
could lead to false positives. For example, a DataFrame with 200 rows split
in chunks of 50 could pass tests expecting DataFrames to contain max 100 rows.

The following tests could have unexpected results:
  - tableRowCountToBeBetween
  - columnValuesSumToBeBetween

Recommended Approach

For datasets that don’t fit in memory and require full-table tests:

Use TestRunner to validate after loading.
Focus DataFrame validation on column-level tests that don’t require aggregation.
Split validation into two phases:
- During ETL: Validate column-level quality with DataFrameValidator.
- After loading: Validate table-level metrics with TestRunner.

Example two-phase approach:

# Phase 1: Validate chunks during ETL (column-level only)
chunk_validator = DataFrameValidator()
chunk_validator.add_tests(
    ColumnValuesToBeNotNull(column="id"),
    ColumnValuesToBeUnique(column="id"),
    ColumnValuesToBeBetween(column="amount", min_value=0)
)

result = chunk_validator.run(
    pd.read_csv("data.csv", chunksize=10000),
    on_success=load_chunk,
    on_failure=rollback
)

# Phase 2: Validate table metrics after loading (table-level)
if result.success:
    from metadata.sdk.data_quality import TestRunner

    table_validator = TestRunner.for_table("Postgres.warehouse.staging.transactions")
    table_validator.add_tests(
        TableRowCountToBeBetween(min_count=10000),
        ColumnValuesSumToBeBetween(column="amount", min_value=1000000)
    )

    # Automatically publishes results for table tests only
    table_results = table_validator.run()

Best Practices

Follow these guidelines to build reliable, production-ready DataFrame validation workflows.

Validate before loading: Catch issues before contaminating your warehouse.

result = validator.validate(transformed_df)
if result.success:
    load_to_warehouse(transformed_df)

Use transactional chunk processing: Ensure atomic all-or-nothing behavior.

with database_transaction():
    result = validator.run(chunks, on_success=load, on_failure=rollback)

Use Collate tests: Let data stewards define quality criteria.
validator.add_openmetadata_table_tests("Table.FQN")
Publish results: Enable tracking and alerting.
result.publish("Table.FQN")

Handle failures gracefully: Don’t silently fail.

if not result.success:
    alert_team()
    rollback_transaction()
    raise DataQualityError(result)

Use appropriate tests for chunks: Avoid full-table tests when processing chunks.

Error Handling

Handle validation errors appropriately:

from metadata.sdk.data_quality.dataframes import DataFrameValidator

try:
    validator = DataFrameValidator()
    validator.add_openmetadata_table_tests("Postgres.warehouse.staging.orders")

    result = validator.validate(df)

    if not result.success:
        # Handle validation failure
        log_validation_failures(result)
        raise ValueError("Data quality checks failed")

except ValueError as e:
    print(f"Configuration error: {e}")
    # Table not found in Collate

except Exception as e:
    print(f"Validation error: {e}")
    # Other errors during validation

Next Steps

Once DataFrame validation is working, explore these related guides.

Review the Test Definitions Reference for all available tests.
Learn about TestRunner for validating tables after loading.
Explore Advanced Usage patterns and configurations.

​DataFrame Validation

​Overview

​Basic Usage

​Creating a Validator

​Adding Tests

​Validating a DataFrame

​Complete ETL Example

​Using Tests from Collate

​Chunk-Based Validation

​Method 1: Manual Chunk Validation

​Method 2: Using the run() Method

​Transaction-Safe Chunk Processing

​Failure Modes

​Working with Validation Results

​Accessing Test Results

​Merging Results from Multiple Chunks

​Publishing Results to Collate

​Important Considerations for Chunk-Based Validation

​Tests That Require Full Table

​Recommended Approach

​Best Practices

​Error Handling

​Next Steps