Open Source Mac Synthetic Data Generation Software
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Synthetic Data Generation Software for Mac
View 3 business solutionsBrowse free open source Synthetic Data Generation software and projects for Mac below. Use the toggles on the left to filter open source Synthetic Data Generation software by OS, license, language, programming language, and project status.
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Automate contact and company data extractionGenerate leads at scale without building or maintaining scrapers. Use 10,000+ ready-made tools that handle authentication, pagination, and anti-bot protection. Pull data from business directories, social profiles, and public sources, then export to your CRM or database via API. Schedule recurring extractions, enrich existing datasets, and integrate with your workflows.
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Mimesis
High-performance fake data generator for Python
Mimesis is an open source high-performance fake data generator for Python, able to provide data for various purposes in various languages. It's currently the fastest fake data generator for Python, and supports many different data providers that can produce data related to people, food, transportation, internet and many more. Mimesis is really easy to use, with everything you need just an import away. Simply import an object, called a Provider, which represents the type of data you need. Mimesis currently supports 34 different locales, the specification of which when creating providers will return data that is appropriate for the language or country associated with that locale. -
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Gretel Synthetics
Synthetic data generators for structured and unstructured text
Unlock unlimited possibilities with synthetic data. Share, create, and augment data with cutting-edge generative AI. Generate unlimited data in minutes with synthetic data delivered as-a-service. Synthesize data that are as good or better than your original dataset, and maintain relationships and statistical insights. Customize privacy settings so that data is always safe while remaining useful for downstream workflows. Ensure data accuracy and privacy confidently with expert-grade reports. Need to synthesize one or multiple data types? We have you covered. Even take advantage or multimodal data generation. Synthesize and transform multiple tables or entire relational databases. Mitigate GDPR and CCPA risks, and promote safe data access. Accelerate CI/CD workflows, performance testing, and staging. Augment AI training data, including minority classes and unique edge cases. Amaze prospects with personalized product experiences. -
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Synthea Patient Generator
Synthetic Patient Population Simulator
SyntheaTM is an open-source, synthetic patient generator that models the medical history of synthetic patients. Our mission is to provide high-quality, synthetic, realistic but not real, patient data and associated health records covering every aspect of healthcare. The resulting data is free from cost, privacy, and security restrictions, enabling research with Health IT data that is otherwise legally or practically unavailable. The models used to generate synthetic patients are informed by numerous academic publications. Our synthetic populations provide insight into the validity of this research and encourage future studies in population health. Synthetic data establishes a risk-free environment for Health IT development and experimentation. This includes the evaluation of new treatment models, care management systems, clinical decision support, and more. -
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Copulas
A library to model multivariate data using copulas
Copulas is a Python library for modeling multivariate distributions and sampling from them using copula functions. Given a table of numerical data, use Copulas to learn the distribution and generate new synthetic data following the same statistical properties. Choose from a variety of univariate distributions and copulas – including Archimedian Copulas, Gaussian Copulas and Vine Copulas. Compare real and synthetic data visually after building your model. Visualizations are available as 1D histograms, 2D scatterplots and 3D scatterplots. Access & manipulate learned parameters. With complete access to the internals of the model, set or tune parameters to your choosing. -
FusionAuth: Authentication and User Management SoftwareFusionAuth adds login, registration, SSO, MFA, and a bazillion other features to your app in days - not months.
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CTGAN
Conditional GAN for generating synthetic tabular data
CTGAN is a collection of Deep Learning based synthetic data generators for single table data, which are able to learn from real data and generate synthetic data with high fidelity. If you're just getting started with synthetic data, we recommend installing the SDV library which provides user-friendly APIs for accessing CTGAN. The SDV library provides wrappers for preprocessing your data as well as additional usability features like constraints. When using the CTGAN library directly, you may need to manually preprocess your data into the correct format, for example, continuous data must be represented as floats. Discrete data must be represented as ints or strings. The data should not contain any missing values. -
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YData Synthetic
Synthetic data generators for tabular and time-series data
A package to generate synthetic tabular and time-series data leveraging state-of-the-art generative models. Synthetic data is artificially generated data that is not collected from real-world events. It replicates the statistical components of real data without containing any identifiable information, ensuring individuals' privacy. This repository contains material related to Generative Adversarial Networks for synthetic data generation, in particular regular tabular data and time-series. It consists a set of different GANs architectures developed using Tensorflow 2.0. Several example Jupyter Notebooks and Python scripts are included, to show how to use the different architectures. YData synthetic has now a UI interface to guide you through the steps and inputs to generate structure tabular data. The streamlit app is available form v1.0.0 onwards. -
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SDGym
Benchmarking synthetic data generation methods
The Synthetic Data Gym (SDGym) is a benchmarking framework for modeling and generating synthetic data. Measure performance and memory usage across different synthetic data modeling techniques – classical statistics, deep learning and more! The SDGym library integrates with the Synthetic Data Vault ecosystem. You can use any of its synthesizers, datasets or metrics for benchmarking. You also customize the process to include your own work. Select any of the publicly available datasets from the SDV project, or input your own data. Choose from any of the SDV synthesizers and baselines. Or write your own custom machine learning model. In addition to performance and memory usage, you can also measure synthetic data quality and privacy through a variety of metrics. Install SDGym using pip or conda. We recommend using a virtual environment to avoid conflicts with other software on your device. -
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Ava: Testdata Xsl
generates Testdata on base of excel: creates xml,excel,csv,html,sql,+
this tool for test-data-generation receives an 'excel-sheet' as primary input. second important paramter is the 'number of test-records to produce'. The excel-data will be reused as long data is needed. This tool is hightly paramatrisazable by the use of 'xsl scripts'. data can be created, updated, modified and finally exported in a format of your choice Main Fuctions: (1) Generates Testdata (excel, xsl, xml) (2) Exports generated testdata in multiple formats (csv, excel, html, sql-insert, individual by xsl extension) (3) Collect all processed data in excel-files (4) plus: Xsl Executor, which let's you run xsl-scripts independently (5) plus: User Interface -
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BlenderProc
Blender pipeline for photorealistic training image generation
A procedural Blender pipeline for photorealistic training image generation. BlenderProc has to be run inside the blender python environment, as only there we can access the blender API. Therefore, instead of running your script with the usual python interpreter, the command line interface of BlenderProc has to be used. In general, one run of your script first loads or constructs a 3D scene, then sets some camera poses inside this scene and renders different types of images (RGB, distance, semantic segmentation, etc.) for each of those camera poses. Usually, you will run your script multiple times, each time producing a new scene and rendering e.g. 5-20 images from it. With a little more experience, it is also possible to change scenes during a single script call, read here how this is done. As blenderproc runs in blenders separate python environment, debugging your blenderproc script cannot be done in the same way as with any other python script. -
Lightspeed golf course management softwareFrom tee sheet management, point of sale and payment processing to marketing, automation, reporting and more—Lightspeed is built for the pro shop, restaurant, back office, beverage cart and beyond.
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DBFeeder
Highly Customizable Test Data Generator
DBFeeder is a great tool to generate synthetic testdata for Oracle Databases and it is ideal for companies who wants to outsource development. Thanks to his original approach, data can be highly customizable and it even fits primary and foreign keys constraints of tables. -
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JRandO is a test data generator or better test object generator framework. It can be used in JUnit tests or in performance test (for e.g. using JMeter). It may also be useful in anonymization of data or in a simulation environment.
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Sample code for JRandO project. (testdata generator, test data generator, test object generator, simulation)
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Synth
The Declarative Data Generator
Synth is an open-source data-as-code tool that provides a simple CLI workflow for generating consistent data in a scalable way. Use Synth to generate correct, anonymized data that looks and quacks like production. Generate test data fixtures for your development, testing, and continuous integration. Generate data that tells the story you want to tell. Specify constraints, relations, and all your semantics. Seed development and environments and CI. Anonymize sensitive production data. Create realistic data to your specifications. Synth uses a declarative configuration language that allows you to specify your entire data model as code. Synth can import data straight from existing sources and automatically create accurate and versatile data models. Synth supports semi-structured data and is database agnostic, playing nicely with SQL and NoSQL databases. Synth supports generation for thousands of semantic types such as credit card numbers, email addresses, and more. -
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Synthetic Data Kit
Tool for generating high quality Synthetic datasets
Synthetic Data Kit is a CLI-centric toolkit for generating high-quality synthetic datasets to fine-tune Llama models, with an emphasis on producing reasoning traces and QA pairs that line up with modern instruction-tuning formats. It ships an opinionated, modular workflow that covers ingesting heterogeneous sources (documents, transcripts), prompting models to create labeled examples, and exporting to fine-tuning schemas with minimal glue code. The kit’s design goal is to shorten the “data prep” bottleneck by turning dataset creation into a repeatable pipeline rather than ad-hoc notebooks. It supports generation of rationales/chain-of-thought variants, configurable sampling, and guardrails so outputs meet format constraints and quality checks. Examples and guides show how to target task-specific behaviors like tool use or step-by-step reasoning, then save directly into training-ready files. -
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A Synthetic Data Generator for producing mixed datasets described by relevant, irrelevant, and redundant features.
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TGAN
Generative adversarial training for generating synthetic tabular data
We are happy to announce that our new model for synthetic data called CTGAN is open-sourced. The new model is simpler and gives better performance on many datasets. TGAN is a tabular data synthesizer. It can generate fully synthetic data from real data. Currently, TGAN can generate numerical columns and categorical columns. TGAN has been developed and runs on Python 3.5, 3.6 and 3.7. Also, although it is not strictly required, the usage of a virtualenv is highly recommended in order to avoid interfering with other software installed in the system where TGAN is run. For development, you can use make install-develop instead in order to install all the required dependencies for testing and code listing. In order to be able to sample new synthetic data, TGAN first needs to be fitted to existing data. -
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Tofu
Tofu is a Python tool for generating synthetic UK Biobank data
Tofu is a Python library for generating synthetic UK Biobank data. The UK Biobank is a large open-access prospective research cohort study of 500,000 middle-aged participants recruited in England, Scotland and Wales. The study has collected and continues to collect extensive phenotypic and genotypic detail about its participants, including data from questionnaires, physical measures, sample assays, accelerometry, multimodal imaging, genome-wide genotyping and longitudinal follow-up for a wide range of health-related outcomes. Tofu will generate synthetic data which conforms to the structure of the baseline data UK Biobank sends researchers by generating random values. For categorical variables (single or multiple choices), a random value will be picked from the UK Biobank data dictionary for that field. For continuous variables, a random value will be generated based on the distribution of values reported for that field on the UK Biobank showcase. -
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Twinify
Privacy-preserving generation of a synthetic twin to a data set
twinify is a software package for the privacy-preserving generation of a synthetic twin to a given sensitive tabular data set. On a high level, twinify follows the differentially private data-sharing process introduced by Jälkö et al.. Depending on the nature of your data, twinify implements either the NAPSU-MQ approach described by Räisä et al. or finds an approximate parameter posterior for any probabilistic model you formulated using differentially private variational inference (DPVI). For the latter, twinify also offers automatic modeling for easy building of models fitting the data. If you have existing experience with NumPyro you can also implement your own model directly. Often data that would be very useful for the scientific community is subject to privacy regulations and concerns and cannot be shared. Differentially private data sharing allows generating of synthetic data that is statistically similar to the original data. -
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Zylthra
Zylthra: A PyQt6 app to generate synthetic datasets with DataLLM.
Welcome to Zylthra, a powerful Python-based desktop application built with PyQt6, designed to generate synthetic datasets using the DataLLM API from data.mostly.ai. This tool allows users to create custom datasets by defining columns, configuring generation parameters, and saving setups for reuse, all within a sleek, dark-themed interface. -
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benerator is a framework for creating realistic and valid high-volume test data, used for load and performance testing and showcase setup. Data is generated from an easily configurable metadata model and exported to databases, XML, CSV or flat files.
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This software allows for a user to generate test data. This is useful for testing Hadoop or other data processing clusters.
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Platform independent, flexible, easy-to-use and free test data generator for database developers and testers, written in pure Java.
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rpackage conjurer
Synthetic data generation using R
Builds synthetic data applicable across multiple domains. This package also provides flexibility to control data distribution to make it relevant to many industry examples.
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