Generate useful synthetic enterprise data with SDV by first defining what the data must support, then preparing accurate metadata, choosing a synthesizer for the data’s shape, encoding essential business rules, and evaluating utility and privacy separately. “Realistic” is not a universal property: a dataset is fit only to the extent that it preserves the patterns and rules required for its intended use, with limitations understood.
1. Define what the synthetic data must do
Start with the use case, not the synthesizer. Data for software testing may need valid keys and realistic edge cases; data for analytics development may need representative distributions and relationships; data for model development may need to preserve patterns relevant to the model task. Data sharing can add separate privacy and disclosure requirements.
Write down the requirements that can be checked. Depending on the use case, these may include:
- Which tables, parent-child relationships, and key behaviors must be represented.
- Which distributions, correlations, rare categories, or boundary cases downstream work depends on.
- Which business rules must always hold, rather than merely occur often.
- What the generated data will be used for, and what it must not be used to infer or claim.
There is no universal acceptance threshold for “realistic.” SDV documents statistical evaluation and customization capabilities, but the criteria for success must come from the task you intend the synthetic data to serve.
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2. Prepare the source data and metadata
SDV is a Python library for synthetic tabular data, with workflows for single tables, sequential data, and multiple related tables. For the Community SDK, SDV’s getting-started documentation gives pip install sdv and recommends using a virtual environment. Check the current installation requirements in the official SDV documentation, since Python support and installation guidance can change between releases.
Metadata describes what the data means structurally: column data types, identifiers, and, for relational data, table relationships and keys. Treat it as modeling input, not administrative paperwork. Incorrect types or relationships can lead to synthetic output that is poorly suited to the intended task.
- Load the source table or tables in your Python workflow.
- Use SDV’s metadata detection as a starting point, not as an unquestioned schema.
- Inspect each column’s semantic data type and correct it where the detected type does not match its meaning.
- Identify primary keys, foreign keys, parent tables, and child tables. Check that the relationships match the actual schema.
- Review sensitive-field annotations and formats, then validate the metadata against the data before fitting a synthesizer.
SDV’s metadata documentation warns that detected metadata may be incomplete or inaccurate. The SDGym metadata guidance also emphasizes accurate descriptions of tables and relationships. In a multi-table schema, a wrong foreign-key relationship is not a cosmetic issue: it changes the structure the workflow is meant to represent.
3. Choose a workflow that matches the data shape
Choose based on the structure you need to generate and evaluate. A single-table synthesizer is a reasonable place to begin for one table; connected enterprise data needs a multi-table workflow with the relationships represented in metadata.
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|---|---|---|
| One table | A single-table synthesizer, such as GaussianCopulaSynthesizer, is a documented option. |
Check that column types, distributions, and the task-relevant patterns are represented adequately. |
| Related enterprise tables | Use a multi-table workflow with relationship metadata; HSASynthesizer is one documented multi-table synthesizer. |
Inspect generated keys, row counts, and parent-child relationship behavior for the application that will consume the data. |
| Sequential data | SDV supports sequential workflows. | Define which time or sequence behavior matters and evaluate it for the intended use; the appropriate configuration depends on the data and task. |
These are workflow examples, not a universal ranking. Table-level relationship fidelity and column-level statistical similarity are different requirements: check both when both matter. Consult the current API documentation for the supported configuration of the synthesizer you select.
4. Encode business rules that metadata cannot express
Types and foreign keys describe the schema, but they do not necessarily capture every rule governing valid records. Identify which requirements are hard constraints and which are patterns that may vary. For example, a rule that only premium accounts may have associated purchases must be enforced if every generated dataset must obey it; a general tendency toward more purchases among premium accounts is a different, statistical requirement.
SDV documents its licensed Constraint Augmented Generation (CAG) bundle for complex multi-table business logic. CAG is not a default capability of every Community installation. SDV Enterprise also describes preprocessing and customization capabilities, but feature and licensing details can change; verify current terms and availability with DataCebo before choosing a tier.
Preprocessing choices can affect the patterns present in generated data. Preserve transformations and constraints that serve the stated use case, and record the choices so that later users know what the dataset was designed to preserve.
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The basic SDV workflow is to fit the selected synthesizer on prepared data, sample synthetic data, and evaluate the result against the requirements defined at the outset. Treat each generated dataset as a candidate, not as a production-equivalent copy.
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- Fit: train the selected synthesizer on the source data and reviewed metadata.
- Sample: generate a synthetic dataset for the intended workflow or evaluation.
- Inspect: check schema, missingness and formats, key behavior, table relationships, and required business rules.
- Evaluate: compare task-relevant statistics and patterns, and examine important edge cases rather than relying on one aggregate score.
- Iterate: revise metadata, constraints, or configuration when a material requirement is not met, then evaluate again.
SDV documents statistical quality evaluation and comparison of real and synthetic data. The useful question is not whether a dataset receives a generic label of “realistic,” but whether the evaluated properties support the specified downstream task. An aggregate score alone cannot establish suitability for every use.
6. Evaluate privacy separately from utility
A synthetic dataset that resembles its source well is not thereby proven private. Utility asks whether important patterns for the task have been retained; privacy asks whether information about people or sensitive records could be disclosed under the relevant threat model. These are separate evaluations.
SDMetrics documents privacy metrics addressing disclosure risks involving sensitive columns and distance-based measures related to overfitting and baseline distances. Its documentation cautions that safety depends on what information must be protected and assumptions about how it might leak. Select checks with those risks in mind; a passing metric is not a legal determination or universal privacy certification.
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If the use case requires a formal record-level guarantee, SDV documents a licensed Differential Privacy bundle. Its documentation describes epsilon differential privacy, with an epsilon privacy-loss budget that controls a privacy-utility tradeoff. SDV also documents a differential privacy evaluation tool. These are not free default features, and current availability and terms should be confirmed with DataCebo.
As SDMetrics puts it, “It’s important to note that safety can be defined in many ways, depending on what type of information is valuable to protect and the assumptions about how it may be leaked.” That distinction is essential when deciding whether ordinary empirical checks are enough or a formal guarantee is required.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Decide whether Community or Enterprise fits
SDV Community is the publicly available Python SDK, distributed under the Business Source License. SDV Enterprise is a licensed offering whose official overview describes scalable synthesis for large numbers of complex, connected tables, richer preprocessing and data understanding, source integrations, and enterprise-wide deployment. The appropriate choice depends on schema scale, required capabilities, deployment needs, and license terms.
Official SDV materials also describe add-on bundles including database connectors, CAG, differential privacy, targeted sampling, and enhanced synthesizers. Do not assume that a bundle is included in a particular tier or currently available on particular terms; confirm the live feature and licensing details before making a purchase or architecture decision.
Quick Recap
Practical release checklist
- The intended use and acceptance criteria are written down.
- Detected metadata has been reviewed and corrected, including semantic types and key relationships.
- The synthesizer matches the data shape, and generated relationships and keys have been inspected where relevant.
- Hard business rules are represented and checked, rather than assumed to emerge from statistical similarity.
- Utility evaluation covers the distributions, correlations, behaviors, and edge cases that matter to the use case.
- Privacy risks have been considered separately, using checks appropriate to the sensitive information and threat model.
- Limitations, configuration choices, and applicable licensing or feature requirements are documented for users of the generated data.
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