Compare Features
Last updated
Last updated
Compare the features available across SDV Community and SDV Enterprise. SDV Enterprise users also have the option of purchasing , which are optional add-on packages for targeted needs.
These synthesizers use AI to learn patterns from your data and use them to recreate synthetic data.
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These synthesizers create random test data based on metadata alone. They do not use AI so you do not need to input any training data.
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These features make it easy to integrate the SDV into your application and pipeline.
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Transformers are used to pre-process your data, which can improve data quality. SDV synthesizers select transformers by default, but you can always customize these to your dataset.
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Transformers are used to pre-process your data, which can improve data quality. SDV synthesizers select transformers by default, but you can always customize these to your dataset.
These transformers are geared towards columns that correspond to industry or domain-specific concepts. Their structure may be human-created.
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Constraints represent business rules and logic that you can apply to your synthesizer.
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Support for custom constraints and additional predefined logic
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Evaluate your synthetic data by comparing it against the real data.
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statistical AI
, , neural networks
advanced Copula modeling with flexible shapes, faster runtime and more
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for separately modeling highly segmented data
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, and Synthesizers for creating synthetic data with differential privacy guarantees
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for sequential data
multi-table for limited tables (<5)
multi-table for unlimited tables
multi-table for unlimited tables
for multi-table synthesizers with various dataset sizes
single table
multi table
using data CSVs or DataFrames
based on your database
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Directly connect to a database for and creating metadata
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Connect to a database for
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for missing value imputation, numerical columns
and statistical transforms
with support for 100+ statistical distributions
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to normalize any distribution with high fidelity
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, , and Encoding for discrete variables ( and )
Encoding including datetime format parsing
for numerical outliers
, , , for adding noise to a column to guarantee differential privacy
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, for normalizing a column while guaranteeing differential privacy
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, for keys and IDs
general-purpose anonymization
for general pseudo-anonymization with a mapping
understanding domains
understanding locations
understanding country and area codes
understanding geographical areas and distances
Predefined logic for individual columns: , , , ,
Predefined logic for multiple columns: , , ,
Write your own
Advanced, predefined logic:
Advanced predefined logic: ,
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Advanced, multi-table logic & algorithms: , , , , and .
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Access to library vendor-agnostic, open source
basic data validity checks , single and multi-table
statistical similarity, single and multi-table
Privacy Metrics: and
1D and 2D bars, scatterplots, heatmaps and more
Use case-specific metrics: ,