# data-centric-ai-community/fg-data-synthetic

**Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/data-centric-ai-community-fg-data-synthetic).**

_How this analysis was created: the description and tags below were written by an AI model that read this project's README and public documentation pages; stars, license and language come straight from the GitHub API. The model does not read the source code._

1,642 stars · 258 forks · Jupyter Notebook · MIT

## Links

- GitHub: https://github.com/Data-Centric-AI-Community/fg-data-synthetic
- Homepage: https://docs.sdk.ydata.ai
- awesome-repositories: https://awesome-repositories.com/repository/data-centric-ai-community-fg-data-synthetic.md

## Topics

`datageneration` `datagenerator` `deep-learning` `gan` `gan-architectures` `gans` `generative-adversarial-network` `machine-learning` `python3` `pytorch` `synthetic-data` `tensorflow2` `time-series` `timeseries` `training-data`

## Description

This project is a synthetic data generator designed to create realistic tabular and time-series datasets for machine learning and testing workflows. It functions as a privacy-preserving platform that models the underlying statistical distributions of source data to produce new records that maintain the original statistical properties and structural integrity.

The tool distinguishes itself by utilizing CPU-optimized statistical sampling, allowing for high-performance data generation on standard hardware without the need for specialized graphics processing units. It employs a configuration-driven, declarative workflow engine that enables users to manage and execute generation pipelines through a command-line interface, removing the requirement for manual coding.

The system incorporates privacy-preserving differential noise to ensure that individual data points cannot be re-identified from the generated sets. It also features schema-aware validation to ensure that all output remains compatible with existing database and machine learning constraints, while its temporal dependency modeling captures sequential patterns to maintain realistic chronological flow in time-series data.

## Tags

### Artificial Intelligence & ML

- [Synthetic Data Generators](https://awesome-repositories.com/f/artificial-intelligence-ml/dataset-generation/synthetic-data-generators.md) — Creates realistic tabular and time-series datasets that preserve the statistical properties of original sources for machine learning.
- [Tabular](https://awesome-repositories.com/f/artificial-intelligence-ml/synthetic-data-generators/tabular.md) — Generates privacy-preserving tabular datasets that maintain the statistical properties and data quality of original sources. ([source](https://github.com/data-centric-ai-community/fg-data-synthetic#readme))
- [Synthetic Time Series Generation](https://awesome-repositories.com/f/artificial-intelligence-ml/synthetic-time-series-generation.md) — Produces synthetic sequences that replicate the temporal patterns and characteristics of original data while protecting sensitive information. ([source](https://github.com/data-centric-ai-community/fg-data-synthetic#readme))
- [CPU-Optimized Statistical Sampling](https://awesome-repositories.com/f/artificial-intelligence-ml/cpu-optimizations/cpu-optimized-statistical-sampling.md) — Generates synthetic data using CPU-optimized mathematical operations to ensure high performance on standard hardware.
- [Machine Learning Data Augmentation](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning-data-augmentation.md) — Expands training datasets with synthetic examples to improve model performance and robustness without requiring additional real-world data collection.
- [Data Synthesis](https://awesome-repositories.com/f/artificial-intelligence-ml/synthetic-data-generators/tabular/data-synthesis.md) — Generates synthetic tables for training machine learning models when original data is restricted by privacy concerns.

### Security & Cryptography

- [Privacy-Preserving Machine Learning](https://awesome-repositories.com/f/security-cryptography/privacy-preserving-machine-learning.md) — Creates realistic datasets that maintain original statistical properties while protecting sensitive information during machine learning development.
- [Differential Privacy Noise Injection](https://awesome-repositories.com/f/security-cryptography/privacy-preserving-utilities/differential-privacy-aggregators/differential-privacy-noise-injection.md) — Ensures privacy by injecting controlled mathematical noise into generated datasets to prevent individual re-identification.
- [Data Privacy Protection Tools](https://awesome-repositories.com/f/security-cryptography/data-privacy-protection-tools.md) — Provides a platform that generates synthetic information to protect sensitive records while maintaining data utility for testing and model training.

### Part of an Awesome List

- [Tabular Data Synthesizers](https://awesome-repositories.com/f/awesome-lists/data/data-tables/tabular-data-models-with-metadata/tabular-data-synthesizers.md) — Produces privacy-compliant tables by modeling the underlying statistical distributions of source data without requiring specialized hardware.
- [Statistical Modeling](https://awesome-repositories.com/f/awesome-lists/devtools/statistical-modeling.md) — Learns the underlying probability distributions of input data to sample new records that maintain original statistical properties.

### Data & Databases

- [High-Performance Data Generation](https://awesome-repositories.com/f/data-databases/high-performance-data-infrastructures/high-performance-data-generation.md) — Enables high-performance generation of realistic datasets using statistical models on standard hardware to accelerate testing and development workflows. ([source](https://github.com/data-centric-ai-community/fg-data-synthetic#readme))
- [Time Series Generation](https://awesome-repositories.com/f/data-databases/time-series-analysis/time-series-generation.md) — Creates synthetic temporal sequences that replicate complex patterns and characteristics found in original time-based datasets.
- [Time-Series Data Simulation](https://awesome-repositories.com/f/data-databases/time-series-data-modeling/time-series-data-simulation.md) — Produces realistic temporal sequences that replicate complex patterns and characteristics of original data for testing and predictive modeling.
- [Time Series Modeling](https://awesome-repositories.com/f/data-databases/time-series-data-modeling/time-series-modeling.md) — Captures sequential correlations and time-based patterns to ensure generated time-series data maintains realistic chronological flow.

### Development Tools & Productivity

- [Synthetic Data CLI Automations](https://awesome-repositories.com/f/development-tools-productivity/cli-workflow-automations/synthetic-data-cli-automations.md) — Provides a command-line interface for configuring and executing automated workflows to create realistic datasets without manual coding. ([source](https://github.com/data-centric-ai-community/fg-data-synthetic#readme))

### DevOps & Infrastructure

- [Declarative Workflow Definitions](https://awesome-repositories.com/f/devops-infrastructure/workflow-orchestrators/declarative-workflow-definitions.md) — Executes data generation pipelines through a configuration-driven engine that maps user-defined parameters to specific statistical modeling tasks.

### Software Engineering & Architecture

- [Schema-Aware Synthesis](https://awesome-repositories.com/f/software-engineering-architecture/data-validation-schemas/schema-aware-synthesis.md) — Validates generated records against predefined structural constraints to ensure compatibility with existing database and machine learning schemas.