Data Validation

Enforce Data Quality with Custom Rules

Define validation rules in SQL. Run them automatically. Get alerted when they fail. Build comprehensive data quality checks that ensure your data meets your standards.

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Validation Results
Order amount positive
0 failures out of 50,234 rows
PASSED
Email format valid
142 failures out of 25,000 rows
FAILED
Customer ID not null
0 failures out of 50,234 rows
PASSED
Foreign key integrity
0 orphaned records
PASSED

Simple, Powerful Validation

1

Write SQL Rules

Define validation rules using SQL you already know. If you can write a WHERE clause, you can write a validation rule.

2

Schedule Runs

Set up validation runs on a schedule that fits your workflow—hourly, daily, or after specific pipeline events.

3

Get Alerted

When validations fail, Sparvi creates issues and alerts your team via Slack, email, or PagerDuty.

Validation Rules You Can Create

Any check you can express in SQL becomes a validation rule.

Not Null Check
-- Ensure customer_id is never null
SELECT COUNT(*)
FROM orders
WHERE customer_id IS NULL
Fails if any orders have null customer IDs
Range Validation
-- Order amounts must be positive
SELECT COUNT(*)
FROM orders
WHERE amount <= 0
Fails if any orders have zero or negative amounts
Referential Integrity
-- All order customers must exist
SELECT COUNT(*)
FROM orders o
LEFT JOIN customers c
  ON o.customer_id = c.id
WHERE c.id IS NULL
Fails if any orders reference non-existent customers
Business Logic
-- Discounts can't exceed order total
SELECT COUNT(*)
FROM orders
WHERE discount > subtotal
Fails if any discounts exceed the subtotal

Why Teams Choose Sparvi for Data Validation

Use SQL you already know—no proprietary language to learn
Automated scheduling runs validations without manual intervention
Track validation history to see trends over time
Failed validations automatically create trackable issues
Integrate with Slack, email, or PagerDuty for instant alerts
Combine with anomaly detection for comprehensive coverage

Validation vs Anomaly Detection

Both are important, but they serve different purposes:

Data Validation

Checks data against known rules. "Orders must have positive amounts." You define the rule.

Anomaly Detection

Finds unexpected patterns. "Order amounts are suddenly 10x higher than normal." ML learns what's normal.

Sparvi provides both—use validation for known rules, anomaly detection for unknown unknowns.

Common Validation Use Cases

Data Completeness

Ensure required fields are populated. Check that critical business data is never missing.

  • • Customer email not null
  • • Order date always present
  • • Product SKU required

Data Accuracy

Verify values fall within acceptable ranges and formats. Catch obvious errors early.

  • • Prices within valid range
  • • Dates not in the future
  • • Email format valid

Data Consistency

Ensure data relationships hold across tables. Maintain referential integrity.

  • • Foreign keys exist
  • • No orphaned records
  • • Totals match sums

Frequently Asked Questions

What is data validation?

Data validation is the process of checking data against defined rules to ensure it meets quality standards. This includes checking for valid formats, acceptable ranges, referential integrity, business logic compliance, and other constraints specific to your data.

Do I need to know SQL to create validation rules?

Yes, Sparvi validation rules are written in SQL. However, if you can write a simple SELECT statement with a WHERE clause, you can create effective validation rules. Most validations are straightforward queries that check for specific conditions.

How often should I run validation rules?

It depends on how frequently your data changes. For transactional data, daily or even hourly validation is common. For more static data, weekly may be sufficient. Sparvi lets you configure schedules per validation rule based on your needs.

What happens when a validation fails?

When a validation fails, Sparvi automatically creates an issue in your issue tracker, records the failure count and details, and can alert your team via Slack, email, or PagerDuty. You can then investigate and resolve the underlying data quality problem.

Start Validating Your Data Today

Define the rules that matter. Automate the checks. Get alerted when they fail.

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Related

What is Data Quality? →Anomaly Detection →Data Profiling →All Features →