DataCebo announced SDV 2.0 on September 15, 2026, describing it as a generally available major release of its SDV Enterprise software. The release focuses on automating how the software understands complex relational databases and generates synthetic data that retains their connected structure. SDV 2.0 is an enterprise Python SDK, not a consumer app; its capabilities and security characteristics below are DataCebo’s descriptions, not independent test results.
What is SDV 2.0?
SDV 2.0 is the latest major release of SDV Enterprise, DataCebo’s commercial synthetic-data software. The company says the enterprise product first launched in 2024 and that experience with customer deployments informed the new release. DataCebo distinguishes SDV Enterprise from SDV Community, which it describes as publicly available software. The announcement and feature list are published on DataCebo’s SDV 2.0 launch post.
The central idea is to model an enterprise database as a connected whole rather than treating each table as an isolated dataset. DataCebo calls this a generative relational model, or GRM: a model trained on relational data to represent its statistical patterns, schema, relationships, context, and business rules. According to the company, a trained GRM can generate a synthetic database or, from a small number of rows, predict and generate related rows and tables. DataCebo presents the model as reusable across multiple downstream applications.
What changed in the 2.0 release?
DataCebo says SDV 2.0 automates more of the work involved in understanding and reproducing complex enterprise data. Its launch materials describe learning patterns across “100’s of tables”; that is a product capability description, not an independently established benchmark.
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Database structure and relationships
The software is described as automatically discovering database structure and connections, including primary, foreign, and composite keys, as well as polymorphic relationships. Its product workflow can connect to databases or load files, then detect metadata, data types, keys, and relationships. These are vendor-described features; published materials do not establish how reliably they perform across every database or schema.
Business rules and constraints
DataCebo says the product can detect business rules embedded in the source data and use them as constraints when generating synthetic records. Users can configure business rules and privacy requirements or use automatic configuration, according to the SDV Enterprise product page. The aim is for generated data to preserve important relationships and rules rather than merely resemble individual columns statistically.
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Scenario-specific data
The announcement also describes generating data for particular scenarios. That can support targeted cases—such as edge conditions—that may be sparse in production data. DataCebo’s launch blog identifies regression testing, performance testing, and edge-case scenario generation as relevant uses.
How does the SDV Enterprise workflow work?
DataCebo describes SDV Enterprise as a Python software development kit installed in the customer’s environment. The broad workflow on its product page is:
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- Connect or load data: connect to a database or load data files in the environment where the enterprise data resides.
- Inspect the structure: allow the software to detect metadata, types, keys, and relationships.
- Set rules and privacy requirements: configure them directly or use the product’s automatic configuration options.
- Train and use the model: train the GRM, then generate synthetic data for the selected application or scenario.
Official SDV documentation says the Enterprise SDK is for licensed users and is installed on-premises. DataCebo says it can run without dedicated GPUs and that inputs, models, and outputs remain within the customer’s security boundary. Those statements describe the vendor’s offering; they are not an absolute security guarantee for every deployment. Organizations still need to assess configuration, access controls, governance, and the privacy risks of their own data and outputs.
What can synthetic relational data be used for?
DataCebo identifies several applications for SDV Enterprise. The common thread is generating data that reflects relationships across enterprise tables while making it available for a task where production records may be difficult to use directly.
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- Software testing: create test data for regression and performance testing, including cases that are uncommon in historical records.
- AI-agent and software development: test software and AI agents against generated data that reflects connected database structures.
- Scenario simulation: generate data for defined situations to explore system behavior or business outcomes.
- AI training and evaluation: use synthetic records in development workflows for training or evaluating AI systems.
- Data sharing: provide synthetic datasets for collaboration where sharing source data is difficult.
Synthetic data is not automatically anonymous or risk-free simply because it is generated. Organizations should evaluate privacy requirements and test outputs for their intended use; DataCebo’s product descriptions do not establish a universal privacy guarantee.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What customer results has DataCebo reported?
DataCebo’s current homepage reports a 100× improvement in test coverage for ING’s SEPA payment application and a 31% improvement in homeowner fraud detection at MAPFRE. These are company-reported customer outcomes tied to those named projects, not general SDV benchmarks or independently validated results. The homepage presents the figures at DataCebo.com.
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On the SDV Enterprise product page, Wim Blommaert, Head of Test Data Management at ING Belgium, says: “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.”
Who should consider SDV 2.0?
SDV 2.0 is most relevant to organizations working with complex, connected relational data that need synthetic datasets for testing, simulation, AI development, or controlled sharing. Its approach may be a better fit for multi-table data than tools designed only around independent single tables, particularly when relationships and business rules matter.
When assessing whether it fits, consider the shape of the data, how much schema and constraint setup the team wants to manage, where the software must run, and the intended output use. DataCebo describes local, on-premises deployment and automated discovery, but organizations should confirm the supported sources, deployment requirements, privacy controls, and licensing terms with the vendor for their own environment.
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