Generating Salesforce Test Data with Snowfakery in mtdt.
Realistic Salesforce test data — generated safely and instantly
Testing Salesforce features properly requires good data.
Flows, validation rules, automation, and integrations all behave differently depending on the structure and volume of records in your org.
But copying production data into sandboxes is risky and often restricted due to privacy or compliance policies.
Creating test data manually is slow and rarely reproduces real-world scenarios.
mtdt solves this with integrated Snowfakery support — allowing teams to generate structured, realistic Salesforce data safely and automatically.
What Is Snowfakery
Snowfakery is an open-source tool designed to generate synthetic Salesforce data.
Instead of copying real records, it creates fake but structurally accurate datasets based on templates.
These templates describe object relationships and record structures — for example:
- Accounts with multiple Contacts
- Contacts connected to Opportunities
- Opportunities linked to Products
The result is realistic datasets that behave like real Salesforce data while remaining fully synthetic.
Snowfakery in mtdt
mtdt integrates Snowfakery directly into the platform so teams can generate and deploy test data as part of their development workflow.
Instead of installing separate tools or scripts, you can run data generation directly from your workspace.
This allows teams to:
- Generate synthetic Salesforce datasets
- Maintain object relationships automatically
- Populate lookup fields using real data from the target org
- Use reusable Snowfakery templates
- Deploy generated records to any Salesforce org
- Recreate datasets whenever needed
How It Works
1. Create a Data Recipe
Snowfakery uses YAML templates that define the structure of generated data.
For example, a template might describe:
- 100 Accounts
- Each Account with several Contacts
- Contacts connected to Opportunities
- Opportunities linked to products
These templates ensure relationships are generated automatically.
2. Generate Data Set
When the recipe runs, mtdt executes the Snowfakery engine and generates data set of records that follow the defined structure.
The generated dataset mirrors realistic Salesforce relationships without using any real customer information.
3. Populate Lookup Fields with Real Org Data
Some test scenarios require generated records to reference data that already exists in the target Salesforce org.
With mtdt, Snowfakery workflows can retrieve real data from the destination org and use it to populate lookup fields during generation.
This makes it possible to combine synthetic test data with real reference records — for example, linking generated records to existing Users, Products, Price Books, or custom reference objects.
The result is more accurate test datasets, with valid relationships to live org data where needed.
4. Deploy to a Salesforce Org
Once generated, the data set can be deployed directly to a selected org:
- Development sandbox
- QA environment
- UAT org
- Training or demo environments
This makes it easy to prepare environments for testing or demonstrations in minutes.
Why It’s a Game Changer
- Privacy-safe testing – No production data is copied or exposed.
- Repeatable datasets – Generate the same structured data whenever needed.
- Relationship-aware data – Complex object hierarchies are created automatically.
- Faster testing workflows – No manual record creation required.
- DevOps-friendly – Templates can be reused and shared across teams.
Real-World Use Cases
QA Testing
Teams can generate hundreds or thousands of records to test flows, triggers, and automation under realistic conditions.
Demo Environments
Create demo-ready Salesforce orgs populated with believable business data.
Integration Testing
Simulate datasets required for external system integrations.
Performance Testing
Generate large datasets to evaluate how automation and queries behave under load.
Summary
The Snowfakery integration in mtdt makes Salesforce test data generation fast, safe, and repeatable.
Instead of relying on copied production data or manually created records, teams can generate structured datasets on demand and deploy them directly to their environments.
Better data leads to better testing — and more reliable deployments.
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