Use Cases & Configuration

Start with your goal. Then pick the first use case to implement. Each use case optimizes for a different outcome. For example,. testing, realism, privacy, consistency, or speed. Each has different configuration requirements.

Getting started

Before you dive into a use case, make sure the basics are covered:

Generation approaches

AI-generated synthesis: best when you need statistical utility with strong privacy, or extra rows.
Rule-based generation: best when values must follow explicit business logic.
Masking / de-identification: best when you need format-preserving replacements and stable keys/relationships.
Hybrid: best when one approach alone does not meet your requirements.

Key configuration decisions

These decisions drive most success (and most rework).

1) Pick the workspace mode that matches your starting point

De-identify: you already have a production-like dataset and mainly need to replace identifiers.
Mock or mask all: you need “production-like” formats but you don’t want to keep original values.
Mock all: you have little/no source data and want to generate everything from scratch.
Synthesize all: you have enough rows and want maximum statistical utility with strong privacy.

2) Decide if you should reshape to a single entity table

AI synthesis works best on a single table. It is often worth creating a SQL view first (especially for ML, analytics and data sharing).

3) Choose masking, rule-based, and AI synthesis

Use masking when downstream systems validate formats (emails, IBANs, UUIDs).
Use rule-based / calculated columns when the business logic must always hold (profit = revenue - costs).
Use AI synthesis when you need privacy + statistical utility for indirect identifiers (age, gender, weight).

Governance, compliance, and automation (reference)

Use workspace roles and sharing to control who can view data and change generators. See Workspace & user management.
Automate workspace setup, scans, and runs via the Syntho REST API.
For deployment options, see Deploy Syntho.

Use cases

Use Case 1: Application & API Testing Privacy-safe, production-like test data for application and API tests in non-production.

Use Case 2: Load & Stress Generate large volumes and edge cases for performance testing without sensitive production data.

Use Case 3: Demo Data Realistic demo data that contains no real identifiers and can be shared safely.

Use Case 4: ETL & Data Pipeline Testing End-to-end pipeline testing without using production data.

Use Case 5: Feature Development Shift-left testing with realistic synthetic data when production data is unavailable or restricted.

Use Case 6: ML Model Development Generate feature datasets when real data is scarce or sensitive.

Use Case 7: Analytics Sandboxes Secure sandboxes for exploratory analytics and data science.

Use Case 8: Cloud & Data Migration Validate data workflows during migrations.

Use Case 9: Data Sharing & Monetization Share data with strong privacy protection.

Use Case 10: Data Subsetting Reduce data footprint while preserving integrity.

Use Case 11: Accelerate PoCs & Pilots Deliver privacy-safe datasets fast to validate ideas, integrations, and workflows.

Use Case 12: Training & Education Create safe, realistic datasets for onboarding, workshops, and hands-on training.

Baseline workflow (applies to every use case)

Use this checklist to go from “use case” to a repeatable job.

Prerequisites

Confirm access, schema alignment, and environment readiness.

Prerequisites

Create a workspace

Pick the source + destination, then choose a workspace mode that matches your starting point.

Configure generators

Start from the simplest approach that meets the goal.

Handle keys and relationships (relational schemas)

Make FK behavior explicit before your first big run.

Validate and sync

Validate early, then resync whenever the schema drifts.

Validate and synchronize workspace

Tune generation settings

Optimize performance and reduce write errors before scaling up.

Last updated 8 months ago

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