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:
Actions to respond to individual validation failures
FinalActions to execute code after your entire validation plan completes
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.
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.
Actions are executed when validation steps fail to meet certain thresholds. Let’s start with a
simple example:
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importpointblankaspbvalidation_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!
deflog_problem():# Simple action that runs when thresholds are exceededprint("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
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.
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.
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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.
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:
defsend_detailed_alert():# Get metadata about the validation failuremetadata=pb.get_action_metadata()# Create a customized alert messageprint(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: 2026-03-13 19:09:29.030877+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.
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:
defstep_alert():metadata=pb.get_action_metadata()print(f"Step {metadata['step']} failed with {metadata['level']} severity")deffinal_summary():summary=pb.get_validation_summary()# Get counts by checking each step's status in the dictionariessteps=range(1,summary['n_steps']+1)n_critical=sum(1forstepinstepsifsummary['dict_critical'].get(step,False))n_error=sum(1forstepinstepsifsummary['dict_error'].get(step,False))n_warning=sum(1forstepinstepsifsummary['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
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:
log detailed information about each failure
send a Slack notification if critical failures occur
generate a comprehensive report after validation completes
deflog_step_failure():metadata=pb.get_action_metadata()print(f"[{metadata['level'].upper()}] Step {metadata['step']}: {metadata['failure_text']}")defanalyze_results():summary=pb.get_validation_summary()# Calculate overall pass ratepass_rate=(summary['n_passing_steps']/summary['n_steps'])*100print(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 notificationifsummary['highest_severity']=="critical":print("\n🚨 [SLACK NOTIFICATION] Critical data quality issues detected!")print("@data-team Please investigate immediately.")# Create our validation workflow with actionsvalidation_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.
Pointblank Validation
2026-03-13|19:09:29
Polarsgame_revenueWARNING0.05ERROR0.1CRITICAL0.15
STEP
COLUMNS
VALUES
TBL
EVAL
UNITS
PASS
FAIL
W
E
C
EXT
#4CA64C
1
col_vals_regex()
Player IDs must follow standard format
player_id
[A-Z]{12}\d{3}
✓
2000
2000 1.00
0 0.00
○
○
○
—
#FF3300
2
col_vals_gt()
Expect that values in item_revenue should be > 0.1.
item_revenue
0.1
✓
2000
1440 0.72
560 0.28
●
●
●
#FF3300
3
col_vals_gt()
Expect that values in session_duration should be > 15.
session_duration
15
✓
2000
1675 0.84
325 0.16
●
●
●
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:
respond immediately to validation failures
customize responses based on severity level
generate comprehensive reports after validation completes
integrate with other systems through custom action functions
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
.
Recently, Rich and I were poking around transit data, and we were struck by the amount of structuring that goes into transit timetables.
For example, consider this weekend rail schedule table from SEPTA, Philadelphia’s transit agency.
Notice these big pieces:
The vertical text on the left indicating trains are traveling “TO CENTER CITY”. The blue header, and spanner columns (“Services” and “Train Number”) grouping related columns. The striped background for easier reading. Also the black background indicating stations in Center City (the urban core). Tables like this often have to be created in tools like Illustrator, and updated by hand. At the same time, when agencies automate table creation, they often sacrifice a lot of the assistive features and helpful affordances of the table.
