Cloud Compliance
Salesforce-Native · AppExchange Certified
DataMasker vs. Data Seeding — Real Data, Real Risk
Seeding tools generate fake data. They don't solve the problem of real PII in sandboxes. DataMasker masks your actual production data, preserving relationships and external IDs. 100% native Salesforce.
100%
Native Salesforce
vs external platforms
3 Weeks
go-live
vs months for seeding setup
5M
records/hour
vs sample data generation
1/3rd
the price
vs enterprise seeding tools
Data seeding tools create fake data that breaks your Salesforce.
Synthetic data generation tools promise clean sandboxes but create three critical problems: broken relationships, unrealistic data shapes, and integration failures. Your testing becomes unreliable.
The Relationship Problem.
Seeding breaks referential integrity. Your sandboxes don't match production relationships.

Data generation creates orphans and broken lookups. Parent-child hierarchies, account-contact relationships, and custom object links fail. Developers waste hours debugging relationship errors that don't exist in production.

"Seeded data had contacts without accounts. Our integration tests kept failing."— Lead Developer, Enterprise SaaS
The Data Shape Gap.
Synthetic data doesn't match production data shapes, skews, and edge cases.

Generated data follows simple patterns. Real production has outliers, historical anomalies, and complex distributions. Bugs found in sandbox don't match production reality. Edge cases in production don't exist in seeded data.

"We found bugs in prod that never showed up in our seeded sandbox. The data shapes were completely different."— QA Manager, FinTech
The Integration Failures.
External IDs, integration keys, and related object hierarchies break with seeded data.

Your integrations depend on consistent external IDs across related records. Seeding tools regenerate these or leave them blank. API calls fail. Data syncs break. External systems can't match records. Everything downstream fails.

"Our ERP integration broke because external IDs changed. We spent a week remapping everything."— Integration Architect, Manufacturing
Before DataMasker → After DataMasker
Before

Fake data. Broken relationships. Missed bugs. Integration failures. Testing doesn't match production.

→
DataMasker

Mask real production data. Preserve all relationships. Maintain external IDs. Test on production-like data.

→
After

Realistic test data. Intact relationships. Working integrations. Production-accurate testing. Zero PII exposure.

How It Works
Mask real data. Keep everything else.
1
Use Production Data

No generation needed. Start with your actual production records, schema, and relationships.

2
Preserve Relationships

Parent-child, lookups, hierarchies—all intact. Referential integrity maintained throughout.

3
Mask with Precision

Field-level rules: replace, erase, anonymize. Format-preserving masking looks real but isn't.

4
Test with Confidence

Production-like data shapes. Real edge cases. Working integrations. Accurate testing results.

Global Tech Company — Testing on Real Data
99M
Records Masked
100%
Relationship Preserved
0
Integration Breaks
3 Weeks
Implementation
Real Data. Real Testing. Real Results.
Six capabilities that make DataMasker the clear alternative to data seeding tools.
Core Capabilities
🔒

Format-Preserving Masking

SSNs look like SSNs. Emails route to your test domain. Phone numbers are valid but fake. Data looks realistic for testing but contains zero PII.

📎

Relationship Preservation

Parent-child, lookups, master-detail, hierarchies—all relationships stay intact. No orphaned records. No broken references. Your data model remains whole.

🔑

External ID Handling

Integration keys, external IDs, and cross-system references are maintained. Your ERP, marketing automation, and custom integrations continue to work.

⚡

Performance at Scale

5M records per hour. 99M records in 24 hours. Bulk API with governor-limit-safe batch processing. Enterprise-scale orgs handled with ease.

⚙

DevOps Integration

REST API triggers from Copado, Gearset, GitLab, Jenkins, AutoRabit. Masking runs as part of your CI/CD pipeline automatically after every refresh.

🛡

Zero Data Movement

100% native Salesforce managed package. No data ever leaves your org. No external APIs, no Cloud Compliance servers. Your CISO will approve.

DataMasker vs. Data Seeding Tools
Feature DataMasker Data Seeding Tools
Native Salesforce ✓ 100% native ✗ External platforms
Real data masking ✓ Masks actual production data ✗ Generates synthetic data
Relationship preservation ✓ All relationships intact ✗ Often breaks referential integrity
External ID support ✓ Integration keys maintained ✗ Regenerated or left blank
Performance 5M records/hour Variable; often slower generation
DevOps integration ✓ REST API triggers — Limited or manual
Implementation time 3 weeks average Months for complex schemas
TCO 1/3rd the price Enterprise seeding tool pricing
Questions About DataMasker vs. Seeding
FAQ
Comparison
Can't we just use Salesforce's sample data?
Salesforce sample data doesn't match your custom schema, fields, or object relationships. It won't work for realistic testing of your actual business processes.
Comparison
What about data generation tools?
Data generation tools create synthetic data but don't solve the PII problem in sandboxes. They also break referential integrity and don't match production data shapes.
Technical
Does masking preserve record IDs?
Yes. Only field values change. Record IDs, relationships, external IDs, and hierarchies remain intact. Your integrations continue to work exactly as they do in production.
Technical
Can we mask selective records?
Yes. DataMasker supports WHERE clause filtering. Mask only specific record sets based on criteria like date ranges, record types, or custom conditions.
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