DataCebo announced SDV 2.0 on September 15, 2026, describing it as a generally available major release of SDV Enterprise. The enterprise software is designed to automate more of the work involved in creating synthetic data from complex relational databases, including discovering structures, relationships and business rules. DataCebo presents it as a Python SDK deployed in a customer’s environment—not a consumer app or a hosted service by default.
What is SDV 2.0?
SDV 2.0 is the new major release of DataCebo’s commercial SDV Enterprise software. DataCebo says the product first launched in 2024 and that deployments highlighted a need to automate more of the process for modeling complex enterprise data. The company distinguishes SDV Enterprise from SDV Community, which it describes as publicly available software. DataCebo’s launch announcement calls SDV 2.0 generally available.
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What is a generative relational model?
DataCebo uses the term generative relational model (GRM) for a generative model trained on relational data that treats a database as a connected whole. Rather than considering each table in isolation, its model is intended to represent statistical patterns, schemas, relationships, context and business rules spanning tables.
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According to DataCebo, a trained GRM can generate a new synthetic database or use a small number of supplied rows to predict and generate related rows and tables. The company’s premise is that one model can then support multiple downstream applications. These are descriptions of the vendor’s approach, not independent assessments of generated-data quality.
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What changes in SDV 2.0?
The release focuses on automating database understanding and the application of constraints. DataCebo says SDV 2.0 can learn patterns across “100’s of tables”; this is a product capability description, not a third-party benchmark. Its announcement and SDV Enterprise product page describe these capabilities:
- Infer complex schemas and data structures, including connections such as primary, foreign and composite keys and polymorphic relationships.
- Detect business rules embedded in data and use them as constraints.
- Learn statistical patterns across many related tables and generate data that follows the configured constraints.
- Create data for particular scenarios, rather than only producing a general-purpose synthetic dataset.
DataCebo describes a workflow that connects a database or loads files, detects metadata, types, keys and relationships, and then allows users to configure business rules and privacy requirements—or use automatic configuration—before training and using a model. This is the vendor’s product description; the available launch materials do not independently verify its performance across different databases or deployments.
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How is SDV Enterprise deployed?
DataCebo describes SDV Enterprise as a downloadable Python SDK installed in a customer’s secure environment, where the enterprise data already resides. Its official documentation says the enterprise SDK is for licensed users and is installed on-premises. The company also says it can run without dedicated GPUs and that inputs, models and outputs remain within the customer’s security boundary.
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What can synthetic enterprise data be used for?
DataCebo positions SDV Enterprise for synthetic data generation, software and AI-agent testing, scenario simulation, AI training and evaluation, and data sharing. Its launch blog highlights regression testing, performance testing and edge-case scenario generation in particular. The intended uses can help teams work with data that reflects connected enterprise structures without making every test or development workflow depend on production records. Whether generated data is suitable for a specific workflow still needs to be assessed against that workflow’s requirements.
Software testing and simulation
For software testing, the stated use is to generate datasets that preserve relationships across tables and can represent selected scenarios, including edge cases. This can support test coverage and repeatable simulations, but DataCebo’s announcement does not establish a universal improvement in test quality or coverage.
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DataCebo also names training and evaluating AI systems and sharing data as use cases. Teams considering these uses should separately validate that the generated data has appropriate statistical utility and does not expose sensitive information. Synthetic generation should not be treated as proof of privacy.
What results has DataCebo reported?
DataCebo’s current homepage attributes a 100× improvement in test coverage for ING’s SEPA payment application to its customer work, and a 31% improvement in homeowner fraud detection to MAPFRE. These are company-reported customer outcomes, not independent benchmarks or results that should be assumed for every SDV deployment. DataCebo’s homepage presents the figures.
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On the SDV Enterprise product page, Wim Blommaert, Head of Test Data Management at ING Belgium, said: “SDV Enterprise is designed for enterprise-scale databases and includes the necessary automation features…SDV is a software development kit; this gives us a lot of flexibility in its use and in our ability to integrate it into our ING landscape.” The ellipsis is part of DataCebo’s published quotation.
What should teams assess before choosing it?
SDV 2.0 is most relevant to organizations that need synthetic data reflecting connected, multi-table databases and want to automate some schema and rule discovery. A practical evaluation should focus on the database and the intended workflow, not just the product’s feature list:
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- Data shape: Does the application need linked tables and cross-table relationships, or is single-table data sufficient?
- Setup effort: Which schemas, keys and business rules can be detected automatically, and which still require configuration or validation?
- Deployment control: Does an on-premises SDK installed in the organization’s environment fit its security and operations requirements?
- Target use: Are the outputs intended for software testing, scenario simulation, AI training or evaluation, or sharing? Each use calls for its own utility and privacy checks.
- Evidence: Do generated datasets meet the organization’s own quality, performance and privacy criteria? Vendor-reported customer figures are not a substitute for validating the product against local data and requirements.
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