Level Up Your Data Validation with Actions and FinalActions

Data validation is only useful if you can respond appropriately when problems arise. That’s why Pointblank’s recent v0.8.0 and v0.8.1 releases have significantly enhanced our action framework, allowing you to create sophisticated, automated responses to validation failures.

In this post, we’ll explore how to use:

1. Actions to respond to individual validation failures
2. FinalActions to execute code after your entire validation plan completes
3. New customization features that make your validation workflows more expressive

Let’s dive into how these features can transform your data validation process from passive reporting to active response.

From Passive Validation to Active Response#

Traditional data validation simply reports problems: “Column X has invalid values.” But what if you want to:

• send a Slack message when critical errors occur?
• log detailed diagnostics about failing data?
• trigger automatic data cleaning processes?
• generate custom reports for stakeholders?

This is where Pointblank’s action system can help. By pairing thresholds with actions, you can create automated responses that trigger exactly when needed.

Getting Started with Actions#

Actions are executed when validation steps fail to meet certain thresholds. Let’s start with a simple example:

import pointblank as pb

validation_1 = (
    pb.Validate(data=pb.load_dataset(dataset="small_table"))
    .col_vals_gt(
        columns="d",
        value=1000,
        thresholds=pb.Thresholds(warning=1, error=5),
        actions=pb.Actions(
            warning="⚠️ WARNING: Some values in column 'd' are below the minimum threshold!"
        )
    )
    .interrogate()
)

validation_1

⚠️ WARNING: Some values in column 'd' are below the minimum threshold!

In this example:

1. we’re validating that values in column “d” are greater than 1000
2. we set a warning threshold of 1 (triggers if any values fail)
3. we define an action that prints a warning message when the threshold is exceeded

Since several values in column d are below 1000, our ‘warning’ action is triggered and the message appears above the validation report.

The Anatomy of Actions#

The Actions class is a very important piece of Pointblank’s response system. Actions can be defined in several ways:

1. String messages: simple text output to the console
2. Callable functions: custom Python functions that execute when triggered
3. Lists of strings/callables: multiple actions that execute in sequence

Actions can be paired with different severity levels:

• ‘warning’: for minor issues that need attention
• ’error’: for more significant problems
• ‘critical’: for severe issues that require immediate action

The v0.8.0 release added two (very) useful new parameters:

• default=: apply the same action to all threshold levels
• highest_only=: only trigger the action for the highest threshold level reached (True by default)

Let’s see how these work in practice:

def log_problem():
    # Simple action that runs when thresholds are exceeded
    print("A validation threshold has been exceeded!")

validation_2 = (
    pb.Validate(
        data=pb.load_dataset(dataset="game_revenue"),
        thresholds=pb.Thresholds(warning=0.05, error=0.10, critical=0.15),
        actions=pb.Actions(default=log_problem)  # Apply this action to all threshold levels
    )
    .col_vals_regex(
        columns="player_id",
        pattern=r"[A-Z]{12}\d{3}"
    )
    .col_vals_gt(
        columns="item_revenue",
        value=0.10
    )
    .interrogate()
)

validation_2

A validation threshold has been exceeded!

In this example, we’re using a simple function that prints a generic message whenever any threshold is exceeded. By using the Actions(default=) parameter, this same function gets applied to all threshold levels (‘warning’, ’error’, and ‘critical’). This saves you from having to define separate actions for each level when you want the same behavior for all of them. The highest_only= parameter (True by default, so not shown here) is complementary and it ensures that only the action for the highest threshold level reached will be triggered, preventing multiple notifications for the same validation failure.

Dynamic Messages with Templating#

Actions don’t have to be static messages. With Pointblank’s templating system, you can create context-aware notifications that include details about the specific validation failure.

Available placeholders include:

• {type}: the validation step type (e.g., "col_vals_gt")
• {level}: the threshold level (‘warning’, ’error’, ‘critical’)
• {step} or {i}: the step number in the validation workflow
• {col} or {column}: the column name being validated
• {val} or {value}: the comparison value used in the validation
• {time}: when the action was executed

You can also capitalize placeholders (like {LEVEL}) to get uppercase text.

action_template = "[{LEVEL}] Step {step}: Values in '{column}' failed validation against {value}."

validation_3 = (
    pb.Validate(
        data=pb.load_dataset(dataset="small_table"),
        thresholds=pb.Thresholds(warning=1, error=4, critical=10),
        actions=pb.Actions(default=action_template)
    )
    .col_vals_lt(
        columns="d",
        value=3000
    )
    .interrogate()
)

validation_3

[ERROR] Step 1: Values in 'd' failed validation against 3000.

This templating approach is a great way to create context-aware notifications that adapt to the specific validation failures occurring. As the example shows, when values in column d fail validation against the limit of 3000, the template automatically generates a meaningful error message showing exactly which step, column, and threshold value was involved.

Accessing Metadata in Custom Action Functions#

For more sophisticated actions, you often need access to details about the validation failure. The get_action_metadata() function provides this context when called inside an action function:

def send_detailed_alert():
    # Get metadata about the validation failure
    metadata = pb.get_action_metadata()

    # Create a customized alert message
    print(f"""
    VALIDATION FAILURE DETAILS
    -------------------------
    Step: {metadata['step']}
    Column: {metadata['column']}
    Validation type: {metadata['type']}
    Severity: {metadata['level']} (level {metadata['level_num']})
    Time: {metadata['time']}

    Explanation: {metadata['failure_text']}
    """)

validation_4 = (
    pb.Validate(
        data=pb.load_dataset(dataset="small_table"),
        thresholds=pb.Thresholds(critical=1),
        actions=pb.Actions(critical=send_detailed_alert)
    )
    .col_vals_gt(
        columns="d",
        value=5000
    )
    .interrogate()
)

validation_4

    VALIDATION FAILURE DETAILS
    -------------------------
    Step: 1
    Column: d
    Validation type: col_vals_gt
    Severity: critical (level 50)
    Time: 2026-05-29 17:44:13.937852+00:00

    Explanation: Exceedance of failed test units where values in `d` should have been > `5000`.

The metadata dictionary contains essential fields for a given validation step, including the step number, column name, validation type, severity level, and failure explanation. This gives you complete flexibility to create highly customized responses based on the specific nature of the validation failure.

Final Actions with FinalActions#

While regular Actions are great for responding to individual validation steps, sometimes you need to take action based on the overall validation results. This is where the new FinalActions feature from v0.8.1 comes in.

Unlike regular Actions that trigger during validation, FinalActions execute after all validation steps are complete. FinalActions accepts any number of actions (strings or callables) and executes them in sequence. Each argument can be a string message to display in the console, a callable function, or a list of strings/callables for multiple actions to execute in sequence.

The real power of FinalActions comes from the ability to access comprehensive information about your validation results using get_validation_summary() . When called inside a function passed to FinalActions , this function provides a dictionary containing counts of passing/failing steps and test units, threshold levels exceeded, and much more:

def generate_summary():
    # Access comprehensive validation results
    summary = pb.get_validation_summary()

    print("\n=== VALIDATION SUMMARY ===")
    print(f"Total steps: {summary['n_steps']}")
    print(f"Passing steps: {summary['n_passing_steps']}")
    print(f"Failing steps: {summary['n_failing_steps']}")

    if summary['highest_severity'] == "critical":
        print("\n⚠️ CRITICAL FAILURES DETECTED - immediate action required!")
    elif summary['highest_severity'] == "error":
        print("\n⚠️ ERRORS DETECTED - review needed")
    elif summary['highest_severity'] == "warning":
        print("\n⚠️ WARNINGS DETECTED - please investigate")
    else:
        print("\n✅ All validations passed!")

validation_5 = (
    pb.Validate(
        data=pb.load_dataset(dataset="small_table"),
        tbl_name="small_table",
        thresholds=pb.Thresholds(warning=1, error=5, critical=10),
        final_actions=pb.FinalActions(
            "Validation process complete.",  # A simple string message
            generate_summary               # Our function using get_validation_summary()
        )
    )
    .col_vals_gt(columns="a", value=1)
    .col_vals_lt(columns="d", value=10000)
    .interrogate()
)

validation_5

Validation process complete.

=== VALIDATION SUMMARY ===
Total steps: 2
Passing steps: 1
Failing steps: 1

⚠️ WARNINGS DETECTED - please investigate

The get_validation_summary() function is only available within functions passed to FinalActions . It gives you access to these key dictionary fields:

• tbl_name: name of the validated table
• n_steps: total number of validation steps
• n_passing_steps, n_failing_steps: count of passing/failing steps
• n, n_passed, n_failed: total test units and their pass/fail counts
• highest_severity: the most severe threshold level reached (‘warning’, ’error’, ‘critical’)
• and many more detailed statistics

This information allows you to create detailed and specific final actions that can respond appropriately to the overall validation results.

Combining Regular and Final Actions#

You can use both Actions and FinalActions together for comprehensive control over your validation workflow:

def step_alert():
    metadata = pb.get_action_metadata()
    print(f"Step {metadata['step']} failed with {metadata['level']} severity")


def final_summary():
    summary = pb.get_validation_summary()

    # Get counts by checking each step's status in the dictionaries
    steps = range(1, summary['n_steps'] + 1)
    n_critical = sum(1 for step in steps if summary['dict_critical'].get(step, False))
    n_error = sum(1 for step in steps if summary['dict_error'].get(step, False))
    n_warning = sum(1 for step in steps if summary['dict_warning'].get(step, False))

    print(f"\nValidation complete with:")
    print(f"- {n_critical} critical issues")
    print(f"- {n_error} errors")
    print(f"- {n_warning} warnings")


validation_6 = (
    pb.Validate(
        data=pb.load_dataset(dataset="small_table"),
        thresholds=pb.Thresholds(warning=1, error=5, critical=10),
        actions=pb.Actions(default=step_alert),
        final_actions=pb.FinalActions(final_summary),
    )
    .col_vals_gt(columns="a", value=5)
    .col_vals_lt(columns="d", value=1000)
    .interrogate()
)

validation_6

Step 1 failed with critical severity
Step 2 failed with error severity

Validation complete with:
- 1 critical issues
- 2 errors
- 2 warnings

This approach allows you to log individual step failures during the validation process using Actions and generate a comprehensive report after all validation steps are complete using FinalActions . Using both action types gives you fine-grained control over when and how notifications and other actions are triggered in your validation workflow.

Real-World Example: Building an Automated Validation Pipeline#

Let’s put everything together in a more realistic example. Imagine you’re validating a gaming revenue dataset and want to:

1. log detailed information about each failure
2. send a Slack notification if critical failures occur
3. generate a comprehensive report after validation completes

def log_step_failure():
    metadata = pb.get_action_metadata()
    print(f"[{metadata['level'].upper()}] Step {metadata['step']}: {metadata['failure_text']}")

def analyze_results():
    summary = pb.get_validation_summary()

    # Calculate overall pass rate
    pass_rate = (summary['n_passing_steps'] / summary['n_steps']) * 100

    print(f"\n==== VALIDATION RESULTS ====")
    print(f"Table: {summary['tbl_name']}")
    print(f"Pass rate: {pass_rate:.2f}%")
    print(f"Failing steps: {summary['n_failing_steps']} of {summary['n_steps']}")

    # In a real scenario, here you might:
    # 1. Save results to a database
    # 2. Generate and email an HTML report
    # 3. Trigger data cleansing workflows

    # Simulate a Slack notification
    if summary['highest_severity'] == "critical":
        print("\n🚨 [SLACK NOTIFICATION] Critical data quality issues detected!")
        print("@data-team Please investigate immediately.")

# Create our validation workflow with actions
validation_7 = (
    pb.Validate(
        data=pb.load_dataset(dataset="game_revenue"),
        tbl_name="game_revenue",
        thresholds=pb.Thresholds(warning=0.05, error=0.10, critical=0.15),
        actions=pb.Actions(default=log_step_failure, highest_only=True),
        final_actions=pb.FinalActions(analyze_results),
        brief=True  # Add automatically-generated briefs
    )
    .col_vals_regex(
        columns="player_id",
        pattern=r"[A-Z]{12}\d{3}",
        brief="Player IDs must follow standard format"  # Custom brief text
    )
    .col_vals_gt(
        columns="item_revenue",
        value=0.10
    )
    .col_vals_gt(
        columns="session_duration",
        value=15
    )
    .interrogate()
)

validation_7

[CRITICAL] Step 2: Exceedance of failed test units where values in `item_revenue` should have been > `0.1`.
[CRITICAL] Step 3: Exceedance of failed test units where values in `session_duration` should have been > `15`.

==== VALIDATION RESULTS ====
Table: game_revenue
Pass rate: 33.33%
Failing steps: 2 of 3

🚨 [SLACK NOTIFICATION] Critical data quality issues detected!
@data-team Please investigate immediately.

Wrapping Up: from Passive Validation to Active Data Quality Management#

With Actions and FinalActions , Pointblank is now more of a complete data quality management system. Instead of just detecting problems, you can now:

1. respond immediately to validation failures
2. customize responses based on severity level
3. generate comprehensive reports after validation completes
4. integrate with other systems through custom action functions
5. automate workflows based on validation results

These capabilities transform data validation from a passive reporting activity into an active component of your data pipeline, helping ensure that data quality issues are detected, reported, and addressed efficiently.

As we continue to enhance Pointblank, we’d love to hear how you’re using Actions and FinalActions in your workflows. Share your experiences or suggestions with us on Discord or file an issue on GitHub .

Learn More#

Explore our documentation to learn more about Pointblank’s action capabilities:

• Actions documentation
• FinalActions documentation
• User Guide on Triggering Actions