To generate useful synthetic enterprise data with SDV, first define what the data must support, then prepare and verify metadata, choose a synthesizer suited to the data’s shape, encode essential business rules, and evaluate utility and privacy separately. “Realistic” is not a universal score: a dataset is useful only to the extent that it preserves the patterns and behavior needed for its intended task.
1. Define what “realistic” means for your use case
Start by naming the job the synthetic data must do. Data for software testing may need valid key relationships and rare edge cases; data for analytics development may need useful distributions and correlations; data for model development may need to preserve patterns relevant to the target and important subgroups. Data sharing raises separate questions about disclosure risk.
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Turn that purpose into acceptance criteria before generating anything. Identify which table relationships, column distributions, correlations, unusual cases, and business rules downstream users depend on. Set criteria for those properties rather than treating one overall similarity score as proof that the dataset is fit for every purpose.
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SDV is a Python library for generating synthetic tabular data, with workflows for single-table, sequential, and multi-table data. Its modeling workflow uses metadata that describes the schema—including column types and identifiers—and, for relational data, table relationships.
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Install the Community SDK
For SDV Community, the official getting-started instructions give pip install sdv and recommend using a Python virtual environment. Check the current SDV documentation for release-specific Python support and installation requirements.
Review the inferred schema
Metadata detection can provide a starting point, but detected metadata is not guaranteed to be complete or accurate. Inspect it against the actual data before fitting a synthesizer. Check column types, identifiers, sensitive-field annotations, and formats, then validate that the metadata describes the data you intend to model.
For a relational schema, verify every parent table, primary key, child table, and foreign key. Incorrect relationships can undermine generated key behavior even if individual columns look plausible. A useful review asks whether each declared key exists, whether the intended parent-child links are represented, and whether the source data actually conforms to those declarations.
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| Data shape | SDV workflow | What to verify |
|---|---|---|
| One table | A single-table synthesizer, such as GaussianCopulaSynthesizer, is a documented starting point for fitting and sampling a table. |
Check the columns and patterns that matter to the intended task; the example is not a universal best choice. |
| Connected tables | Use multi-table metadata and a multi-table synthesizer. HSASynthesizer is one documented option. |
Check generated keys, row counts, and parent-child behavior in the resulting data, not just similarity among individual columns. |
| Sequential records | SDV supports sequential-data workflows. | Confirm that the workflow and evaluation address the sequence structure required by your use case. |
Table relationships and column-level statistical similarity are different properties. A relational model should represent the key graph correctly, but that alone does not establish that useful distributions, correlations, or application behavior have been preserved.
4. Encode business logic the schema cannot express
Column types and foreign keys describe structure; they do not necessarily capture every rule that makes records valid for a business process. Decide which rules are essential to the intended use, and check generated data against them.
SDV documents a licensed Enterprise Constraint Augmented Generation (CAG) bundle for complex multi-table business logic. Its example describes a rule where only premium accounts may have associated purchases. CAG is not a capability to assume is included in every Community installation; verify current licensing and availability with DataCebo.
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Preprocessing and customization also affect generated patterns. Keep transformations and rules that serve the task, and document consequential choices so users understand how the data was prepared.
5. Fit, sample, inspect, and iterate
- Prepare the source and metadata. Resolve schema issues and confirm the intended table relationships before modeling.
- Fit the selected synthesizer. Use the workflow appropriate to a single table, relational tables, or sequential data.
- Sample synthetic data. Generate an amount suitable for the planned checks and downstream task.
- Inspect application requirements. Check schema conformity, key behavior, row counts, required business rules, and relevant edge cases.
- Evaluate and revise. Use the results to adjust metadata, constraints, preprocessing, or synthesizer choice, then repeat the checks.
This is an iterative modeling workflow, not a guarantee that the output will match production data or work for every downstream purpose.
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6. Evaluate utility and privacy as separate questions
Does it preserve the patterns the task needs?
SDV documents statistical quality evaluation and comparison of real and synthetic data. Select diagnostics that correspond to your acceptance criteria: for example, inspect relevant distributions and relationships, then check the rare cases or behaviors that could materially affect the task. An aggregate score cannot establish suitability for all uses, and statistical similarity does not by itself demonstrate privacy.
Could it disclose sensitive information?
SDMetrics documents privacy metrics for disclosure risks involving sensitive columns and distance-based measures related to overfitting and baseline distances. Choose checks in light of the sensitive information and plausible threat model. As the SDMetrics documentation cautions, “safety can be defined in many ways,” depending on what information is valuable to protect and assumptions about how it may be leaked. A passing metric is not a legal certification or a universal guarantee that disclosure is impossible.
For a use case requiring a formal record-level guarantee, SDV documents a licensed Differential Privacy bundle. Its documentation describes epsilon differential privacy, where an epsilon privacy-loss budget controls a privacy-versus-quality tradeoff; SDV also documents a differential privacy evaluation tool. This is not a free default capability, so check current availability and licensing before planning around it.
7. Decide whether Community or Enterprise fits
SDV Community is the publicly available Python SDK and is distributed under the Business Source License. SDV Enterprise is a licensed offering whose overview describes capabilities for large numbers of complex connected tables, richer preprocessing and data understanding, integrations, and enterprise-wide deployment. The exact feature set, bundle availability, and license terms may change, so verify them with DataCebo before selecting a tier.
For an initial proof of concept, Community may be sufficient if its current capabilities fit the schema and evaluation needs. Enterprise is relevant when scale, integrations, advanced preprocessing, or licensed add-ons such as CAG or differential privacy are requirements—not simply because the source data is called “enterprise.”
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