9 Open-Source Tools to Generate Synthetic Data 9 Open-Source Tools to Generate Synthetic Data
In today’s data-driven world, everyone needs data, but sometimes you may not have a lot to work with. This is where... 9 Open-Source Tools to Generate Synthetic Data

In today’s data-driven world, everyone needs data, but sometimes you may not have a lot to work with. This is where synthetic data comes into play! So, what is it and how can you generate synthetic data? With these data sets, concerns about privacy, compliance, and other issues are easily mitigated.

So let’s take a look at a couple of tools that might be driving the future of reporting and automation by being able to generate synthetic data.

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Conditional Table Generation using Adversarial Networks or CTGAN for short, is a groundbreaking tool that utilizes the power of Generative Adversarial Networks to generate highly realistic and coherent synthetic tabular data. Unlike traditional data generation methods, CTGAN excels at handling complex datasets characterized by intricate relationships and dependencies among features.

At the core of CTGAN lies a carefully crafted architecture consisting of two neural networks: a generator and a discriminator. The generator network, trained using a conditional adversarial loss function, learns to create synthetic data samples that closely resemble the real dataset. On the other hand, the discriminator network aims to distinguish between real and synthetic samples, providing valuable feedback to the generator network during the training process.

One of the key strengths of CTGAN is its ability to capture complex data distributions. By leveraging the adversarial training procedure, CTGAN learns to generate synthetic data that preserves the underlying statistical properties of the real dataset, including correlations, marginal distributions, and higher-order interactions. CTGAN has a wide range of potential applications, including data augmentation, missing data imputation, and privacy-preserving data publishing. In data augmentation, CTGAN can be used to generate additional data samples to enhance the performance of machine learning models trained on limited datasets. 


DoppelGANger uses GANs to create synthetic data for time-series applications. It’s particularly useful for generating data in fields such as finance and IoT. It skillfully captures the intricate patterns and dependencies found in real-world data, making it an invaluable tool for various time-series applications.

One of the key strengths of DoppelGANger lies in its ability to address the scarcity of labeled data, which often hinders the development of accurate machine learning models. By generating realistic and diverse synthetic data, DoppelGANger empowers researchers and practitioners to train and evaluate models more effectively, even in data-scarce scenarios.

By leveraging the capabilities of GANs, DoppelGANger offers a powerful solution for generating high-quality synthetic time-series data. Its versatility, open-source nature, and ability to address data scarcity make it a valuable asset for a wide range of applications, from finance to IoT. 


Synner’s primary focus is to provide businesses, researchers, and individuals with a comprehensive solution that empowers them to generate vast amounts of high-quality synthetic data efficiently and effortlessly.

One of Synner’s key strengths lies in its ability to create diverse and intricate datasets that accurately reflect real-world scenarios. Leveraging advanced algorithms and techniques, it can generate synthetic data that mimics the characteristics, patterns, and relationships found in authentic datasets. This enables organizations to thoroughly test and evaluate their systems, applications, and models, ensuring their accuracy, reliability, and robustness.

On an interesting note, Synner offers a user-friendly interface and intuitive workflow, making it accessible to individuals with varying levels of technical expertise. Users can easily define their data generation parameters, preview the generated data, and export it in a wide range of formats, including CSV, JSON, and SQL.


Synthea is an open-source synthetic patient generator aimed at healthcare research and simulation. It enables the creation of vast and diverse virtual patient populations with intricate medical histories, demographics, and clinical data. These synthetic patients mirror real-world patient characteristics, allowing researchers, clinicians, and educators to conduct comprehensive studies, test interventions, and simulate healthcare scenarios without compromising patient privacy.

Synthea leverages advanced algorithms and machine learning techniques to generate synthetic data of patients that closely resembles actual medical records. It employs natural language processing to create realistic patient narratives, diagnoses, procedures, and medications. The synthetic data patients exhibit a wide range of conditions, from common illnesses to rare diseases, ensuring that researchers can explore a broad spectrum of healthcare scenarios.

One of the key strengths of Synthea is its ability to model complex patient journeys over time. It stimulates the progression of chronic diseases, captures the impact of lifestyle factors, and incorporates patient-provider interactions. 

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The SDV is a framework designed to address the growing need for high-quality and diverse synthetic data. Developed by a team of data scientists and researchers, SDV provides a comprehensive solution for generating realistic and representative synthetic data across a wide range of domains and applications.

At its core, SDV incorporates multiple models and techniques to ensure the generation of synthetic data that closely resembles real-world data in terms of statistical properties, distributions, and relationships between variables. These models include generative adversarial networks (GANs), variational autoencoders (VAEs), and copula-based methods, among others. By leveraging these advanced techniques, SDV can capture complex patterns and structures within the data, enabling the generation of synthetic data that is both diverse and consistent.


TGAN is a synthetic data generation tool that leverages the power of Generative Adversarial Networks to tackle the unique challenges of tabular data with high-dimensional features. Its effectiveness lies in its ability to maintain the statistical properties of the original data while generating realistic and diverse synthetic samples.

TGAN relies on the adversarial nature of GANs, where two neural networks, a generator, and a discriminator, engage in a competitive game. The generator aims to create synthetic data samples that closely resemble the real data distribution, while the discriminator strives to distinguish between real and synthetic samples. One of the key strengths of TGAN is its ability to handle high-dimensional tabular data, which is often encountered in domains such as finance, healthcare, and e-commerce. 


MirrorDataGenerator is a tool that prioritizes privacy preservation. Its primary objective is to create synthetic data that closely resembles the original dataset in terms of utility and structure while safeguarding sensitive information. This approach empowers businesses and organizations to leverage data-driven insights without compromising the privacy of individuals.

Central to MirrorDataGenerator’s functionality is its ability to generate synthetic data that retains the statistical properties and relationships of the original dataset. This is achieved through advanced machine learning algorithms that analyze and learn from the underlying patterns and correlations present in the original data. As a result, the synthetic data generated by MirrorDataGenerator accurately reflects the distribution and characteristics of the original dataset, making it suitable for various downstream tasks such as model training, testing, and analysis.

With its focus on privacy preservation and customizable controls, this tool empowers organizations to unlock the potential of data-driven insights while maintaining the highest standards of privacy and compliance.


Plaitpy generates realistic synthetic data for use in software testing and machine learning. It aims to create data that closely mimics real-world scenarios. The goal is to address the increasing need for realistic synthetic data in software testing and machine learning, which enables developers and data scientists to conduct comprehensive testing and develop accurate models.

Plaitpy’s strengths lie in its ability to create synthetic data that exhibits the same statistical properties and complexities as real-world data. This is achieved through the implementation of advanced algorithms and techniques that meticulously replicate the characteristics of real-world data, including distributions, correlations, and patterns. As a result, Plaitpy-generated data can effectively simulate real-world conditions, allowing for rigorous testing and model validation.

While Plaitpy primarily focuses on software testing and machine learning applications, its potential extends beyond these domains. It can be leveraged in various fields that require the generation of synthetic data, such as data augmentation, privacy preservation, and cybersecurity.


SmartNoise is an innovative project by OpenDP, that looks to focus on data privacy and analysis. It operates on the principle of differential privacy, a technique that safeguards sensitive information while preserving its utility for analysis. By leveraging SmartNoise, organizations can unlock the potential of synthetic data generation, enabling them to harness the power of data without compromising privacy.

SmartNoise offers multiple advantages. Firstly, it empowers organizations to comply with stringent data privacy regulations, such as the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA) in the United States. By utilizing synthetic data, organizations can avoid privacy breaches and potential legal repercussions, fostering trust with customers and stakeholders.

Secondly, SmartNoise empowers businesses to unlock the full potential of their data. Synthetic datasets generated through SmartNoise can be used for various analytical purposes, including machine learning, statistical modeling, and risk assessment. This enables organizations to make data-driven decisions while safeguarding individual privacy.

Finally, SmartNoise promotes collaboration and data sharing. Sensitive data can be transformed into synthetic datasets, allowing organizations to collaborate with partners and researchers without exposing confidential information. 

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Interested in learning more about how to generate synthetic data and its potential to change the world of data science? Then you want to be where the action is at, where the leading minds come together to discuss, work, and push the envelope forward in the realm of synthetic data. Sound like a plan?

With that said, if generating synthetic data is that goal, then you don’t want to miss ODSC West this October 29th-31st or ODSC Europe from September 5th-6th.

At ODSC West and ODSC Europe, you’ll get to touch base with some of the leading minds in AI who are spearheading the latest advancements, theories, and technology. And in the world of synthetic data, staying on top of the latest in AI is critical for success in the role.

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