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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Input business rules into your synthesizer using constraints. This ensures high-quality, valid synthetic data, 100% of the time.
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Support for programming your constraint 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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for modeling data with a few rows
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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 tables: , , , ,
for single tables
Advanced, predefined logic for individual tables:
Advanced predefined logic for individual tables: ,
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Advanced, predefined logic for multi-table tables: , , , , and .
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for multi-table
Access to library vendor-agnostic, open source
basic data validity checks , single and multi-table
statistical similarity, single and multi-table
Measure the privacy of your data: and
of any synthesizer algorithm
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1D and 2D bars, scatterplots, heatmaps and more
Use case-specific metrics: ,