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Single Table
Learn a tabular model to synthesize rows in a table
Multi Table
Learn a relational data model to synthesize multiple, related tables
Sequential Table
Learn a sequential or time series model to synthesize new events
The SDV ecosystem
Public, Source-Available Libraries
The SDV is an overall ecosystem for synthetic data models, benchmarks, and metrics. Explore publicly available libraries supporting the SDV. Each can be used as standalone packages for particular needs.
The Synthetic data vault
What can you use synthetic data for?
Use a synthetic data in place of real data for added protection, or use it in addition to your real data as an enhancement.
SDV case studies
Synthetic data helps banks detect money laundering without compromising privacy
Learn more
MAPFRE: improving detection of homeowner insurance fraud by 31 percent with synthetic data
Learn moreLatest news and updates
from sdv.datasets.demo import download_demo
from sdv.single_table import GaussianCopulaSynthesizer
real_data, metadata = download_demo(
'single_table', 'fake_hotel_guests')
synthesizer = GaussianCopulaSynthesizer(metadata)
synthesizer.fit(real_data)
synthetic_data = synthesizer.sample(num_rows=10)Follow us
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