Synthetic Data Vault
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  • Welcome to the SDV!
  • Tutorials
  • Explore SDV
    • SDV Community
    • SDV Enterprise
      • ⭐Compare Features
    • SDV Bundles
      • ❖ AI Connectors
      • ❖ CAG
      • ❖ Differential Privacy
      • ❖ XSynthesizers
  • Single Table Data
    • Data Preparation
      • Loading Data
      • Creating Metadata
    • Modeling
      • Synthesizers
        • GaussianCopulaSynthesizer
        • CTGANSynthesizer
        • TVAESynthesizer
        • ❖ XGCSynthesizer
        • ❖ SegmentSynthesizer
        • * DayZSynthesizer
        • ❖ DPGCSynthesizer
        • ❖ DPGCFlexSynthesizer
        • CopulaGANSynthesizer
      • Customizations
        • Constraints
        • Preprocessing
    • Sampling
      • Sample Realistic Data
      • Conditional Sampling
    • Evaluation
      • Diagnostic
      • Data Quality
      • Visualization
  • Multi Table Data
    • Data Preparation
      • Loading Data
        • Demo Data
        • CSV
        • Excel
        • ❖ AlloyDB
        • ❖ BigQuery
        • ❖ MSSQL
        • ❖ Oracle
        • ❖ Spanner
      • Cleaning Your Data
      • Creating Metadata
    • Modeling
      • Synthesizers
        • * DayZSynthesizer
        • * IndependentSynthesizer
        • HMASynthesizer
        • * HSASynthesizer
      • Customizations
        • Constraints
        • Preprocessing
      • * Performance Estimates
    • Sampling
    • Evaluation
      • Diagnostic
      • Data Quality
      • Visualization
  • Sequential Data
    • Data Preparation
      • Loading Data
      • Cleaning Your Data
      • Creating Metadata
    • Modeling
      • PARSynthesizer
      • Customizations
    • Sampling
      • Sample Realistic Data
      • Conditional Sampling
    • Evaluation
  • Concepts
    • Metadata
      • Sdtypes
      • Metadata API
      • Metadata JSON
    • Constraints
      • Predefined Constraints
        • Positive
        • Negative
        • ScalarInequality
        • ScalarRange
        • FixedIncrements
        • FixedCombinations
        • ❖ FixedNullCombinations
        • ❖ MixedScales
        • OneHotEncoding
        • Inequality
        • Range
        • * ChainedInequality
      • Custom Logic
        • Example: IfTrueThenZero
      • ❖ Constraint Augmented Generation (CAG)
        • ❖ CarryOverColumns
        • ❖ CompositeKey
        • ❖ ForeignToForeignKey
        • ❖ ForeignToPrimaryKeySubset
        • ❖ PrimaryToPrimaryKey
        • ❖ PrimaryToPrimaryKeySubset
        • ❖ SelfReferentialHierarchy
        • ❖ ReferenceTable
        • ❖ UniqueBridgeTable
  • Support
    • Troubleshooting
      • Help with Installation
      • Help with SDV
    • Versioning & Backwards Compatibility Policy
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On this page
  • AI-Based Synthesizers
  • Test Data Synthesizers
  • Data Integrations
  • Pre-Process Statistical Information
  • Understand & Anonymize Real-World Concepts
  • Constraints
  • Synthetic Data Evaluation
  1. Explore SDV
  2. SDV Enterprise

Compare Features

PreviousSDV EnterpriseNextSDV Bundles

Last updated 21 days ago

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.

AI-Based Synthesizers

These synthesizers use AI to learn patterns from your data and use them to recreate synthetic data.

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Test Data Synthesizers

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.

SDV Community
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Data Integrations

These features make it easy to integrate the SDV into your application and pipeline.

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Pre-Process Statistical Information

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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Understand & Anonymize Real-World Concepts

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

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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Synthetic Data Evaluation

Evaluate your synthetic data by comparing it against the real data.

Public SDV
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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: ,

⭐
SDV Bundles
GaussianCopula
CTGAN
TVAE
CopulaGAN
XGC
XSynthesizers bundle
SegmentSynthesizer
XSynthesizers bundle
DPGC
DPGCFlex
Differential Privacy bundle
PAR
HMA
HSA
Independent
Performance estimates
DayZSynthesizer
DayZSynthesizer
Auto-detect metadata
AI Connectors bundle
AI Connectors bundle
AI Connectors bundle
FloatFormatter
ClusterBasedNormalizer
GaussianNormalizer
XGaussianNormalizer
XSynthesizers bundle
ECDFNormalizer
XSynthesizers bundle
Uniform
Label
OneHot
Datetime
OutlierEncoder
DPLaplaceNoiser
DPTimestampLaplaceNoiser
DPResponseRandomizer
DPWeightedResponseRandomizer
Differential Privacy bundle
DPECDFNormalizer
DPDiscreteECDFNormalizer
Differential Privacy bundle
RegexGenerator
IDGenerator
AnonymizedFaker
PsuedoAnonymizedFaker
Emails
Addresses
Phone Numbers
GPS Coordinates
FixedIncrements
Negative
Positive
ScalarInequality
ScalarRange
FixedCombinations
Inequality
OneHotEncoding
Range
custom constraints
ChainedInequality
FixedNullCombinations
MixedScales
CAG bundle
CarryOverColumns
CompositeKey
ForeignToPrimaryKeySubset
UniqueBridgeTable
more
CAG bundle
SDMetrics
Diagnostic Report
Quality Report
DisclosureProtection
DisclosureProtectionEstimate
Visualization
OutlierCoverage
SmoothnessSimilarity
importing real data
exporting synthetic data
Auto-detect metadata