> For the complete documentation index, see [llms.txt](https://docs.syntho.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.syntho.ai/overview/get-started/use-cases-and-configuration.md).

# 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:

* [Prerequisites](/overview/get-started/prerequisites.md)
* [Deploy Syntho](/deploy-syntho/introduction.md)

#### 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).

* [Use SQL views as input tables](/setup-workspaces/create-a-workspace/use-sql-views-as-input-tables.md)
* [Cross-table relationships limitations](/configure-a-data-generation-job/configure-column-settings/ai-powered-generation/table-relationships.md)

**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](/overview/get-started/syntho-bootcamp/8.-workspace-and-user-management.md).
* Automate workspace setup, scans, and runs via the [Syntho REST API](/syntho-api/syntho-rest-api.md).
* For deployment options, see [Deploy Syntho](/deploy-syntho/introduction.md).

### Use cases

<table data-card-size="large" data-view="cards"><thead><tr><th></th><th data-hidden data-card-cover data-type="image">Cover image</th><th data-hidden></th><th data-hidden></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><strong>Use Case 1: Application &#x26; API Testing</strong><br>Privacy-safe, production-like test data for application and API tests in non-production.</td><td><a href="/files/I0kbZfdGenRDB21GS5vO">/files/I0kbZfdGenRDB21GS5vO</a></td><td></td><td></td><td><a href="/pages/034fkmKsQX3DK0iB7UVm">/pages/034fkmKsQX3DK0iB7UVm</a></td></tr><tr><td><strong>Use Case 2: Load &#x26; Stress</strong><br>Generate large volumes and edge cases for performance testing without sensitive production data.</td><td><a href="/files/GzMpv3u3uevUiBGFKFqD">/files/GzMpv3u3uevUiBGFKFqD</a></td><td></td><td></td><td><a href="/pages/NkTf8L2XkXSNqgHYP8MA">/pages/NkTf8L2XkXSNqgHYP8MA</a></td></tr><tr><td><strong>Use Case 3: Demo Data</strong><br>Realistic demo data that contains no real identifiers and can be shared safely.</td><td><a href="/files/ujHbESJyWHjW78cL7Qu6">/files/ujHbESJyWHjW78cL7Qu6</a></td><td></td><td></td><td><a href="/pages/gtzgHUwDJVk6MFeP72L9">/pages/gtzgHUwDJVk6MFeP72L9</a></td></tr><tr><td><strong>Use Case 4: ETL &#x26; Data Pipeline Testing</strong><br>End-to-end pipeline testing without using production data.</td><td><a href="/files/oaK560iE4ICpL1AhHdLO">/files/oaK560iE4ICpL1AhHdLO</a></td><td></td><td></td><td><a href="/pages/m0lRco6Dnu1jsuaHJaEA">/pages/m0lRco6Dnu1jsuaHJaEA</a></td></tr><tr><td><strong>Use Case 5: Feature Development</strong><br>Shift-left testing with realistic synthetic data when production data is unavailable or restricted.</td><td><a href="/files/GzMpv3u3uevUiBGFKFqD">/files/GzMpv3u3uevUiBGFKFqD</a></td><td></td><td></td><td><a href="/pages/OABjH6uhizuKtzt5cuCG">/pages/OABjH6uhizuKtzt5cuCG</a></td></tr><tr><td><strong>Use Case 6: ML Model Development</strong><br>Generate feature datasets when real data is scarce or sensitive.</td><td><a href="/files/ujHbESJyWHjW78cL7Qu6">/files/ujHbESJyWHjW78cL7Qu6</a></td><td></td><td></td><td><a href="/pages/c4s5IDwQiiYHo4SD8Lb8">/pages/c4s5IDwQiiYHo4SD8Lb8</a></td></tr><tr><td><strong>Use Case 7: Analytics Sandboxes</strong><br>Secure sandboxes for exploratory analytics and data science.</td><td><a href="/files/ujHbESJyWHjW78cL7Qu6">/files/ujHbESJyWHjW78cL7Qu6</a></td><td></td><td></td><td><a href="/pages/n2MVcgykJHrCjz460ag5">/pages/n2MVcgykJHrCjz460ag5</a></td></tr><tr><td><strong>Use Case 8: Cloud &#x26; Data Migration</strong><br>Validate data workflows during migrations.</td><td><a href="/files/q1kuF8aCbmqiHC1gUDLz">/files/q1kuF8aCbmqiHC1gUDLz</a></td><td></td><td></td><td><a href="/pages/P8PBQJs0PbTAZSlhQmlX">/pages/P8PBQJs0PbTAZSlhQmlX</a></td></tr><tr><td><strong>Use Case 9: Data Sharing &#x26; Monetization</strong><br>Share data with strong privacy protection.</td><td><a href="/files/q1kuF8aCbmqiHC1gUDLz">/files/q1kuF8aCbmqiHC1gUDLz</a></td><td></td><td></td><td><a href="/pages/qjGJAhVnPr3eAhxOw7do">/pages/qjGJAhVnPr3eAhxOw7do</a></td></tr><tr><td><strong>Use Case 10: Data Subsetting</strong><br>Reduce data footprint while preserving integrity.</td><td><a href="/files/pRyl3u4klwSFcFnF2Lrk">/files/pRyl3u4klwSFcFnF2Lrk</a></td><td></td><td></td><td><a href="/pages/xq3wmL0rf8lmm7PUdIDe">/pages/xq3wmL0rf8lmm7PUdIDe</a></td></tr><tr><td><strong>Use Case 11: Accelerate PoCs &#x26; Pilots</strong><br>Deliver privacy-safe datasets fast to validate ideas, integrations, and workflows.</td><td><a href="/files/q1kuF8aCbmqiHC1gUDLz">/files/q1kuF8aCbmqiHC1gUDLz</a></td><td></td><td></td><td><a href="/pages/dRqPKvaPcwWLB81BtcED">/pages/dRqPKvaPcwWLB81BtcED</a></td></tr><tr><td><strong>Use Case 12: Training &#x26; Education</strong><br>Create safe, realistic datasets for onboarding, workshops, and hands-on training.</td><td><a href="/files/pRyl3u4klwSFcFnF2Lrk">/files/pRyl3u4klwSFcFnF2Lrk</a></td><td></td><td></td><td><a href="/pages/pGk7k01EA5f5uwtQyyfs">/pages/pGk7k01EA5f5uwtQyyfs</a></td></tr></tbody></table>

<details>

<summary>Baseline workflow (applies to every use case)</summary>

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

{% stepper %}
{% step %}

### Prerequisites

Confirm access, schema alignment, and environment readiness.

* [Prerequisites](/overview/get-started/prerequisites.md)
  {% endstep %}

{% step %}

### Create a workspace

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

* [Create a workspace](/setup-workspaces/create-a-workspace.md)
* [Workspace modes](/setup-workspaces/create-a-workspace/workspace-modes.md)
  {% endstep %}

{% step %}

### Configure generators

Start from the simplest approach that meets the goal.

* [Introduction to data generators](/overview/get-started/introduction-to-data-generators.md)
* [Generators](/configure-a-data-generation-job/configure-column-settings.md)
  {% endstep %}

{% step %}

### Handle keys and relationships (relational schemas)

Make FK behavior explicit before your first big run.

* [Referential integrity & foreign keys](/overview/get-started/syntho-bootcamp/6.-referential-integrity-and-foreign-keys.md)
* [Manage foreign keys](/configure-a-data-generation-job/manage-foreign-keys.md)
* [Key generators](/configure-a-data-generation-job/configure-column-settings/key-generators.md)
  {% endstep %}

{% step %}

### Validate and sync

Validate early, then resync whenever the schema drifts.

* [Validate and synchronize workspace](/configure-a-data-generation-job/generation-and-validation/validate-and-synchronize-workspace.md)
  {% endstep %}

{% step %}

### Tune generation settings

Optimize performance and reduce write errors before scaling up.

* [View and adjust generation settings](/configure-a-data-generation-job/generation-and-validation/view-and-adjust-generation-settings.md)
* [Large workloads](/overview/get-started/syntho-bootcamp/9.-large-workloads.md)
  {% endstep %}
  {% endstepper %}

</details>


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