How do I get synthetic data?

Synthetic data is artificially generated data that mimics real-world data without necessarily using actual people's records. You can get it in several ways, depending on what you need it for. ## 4 ways to get synthetic data 1. Generate it with AI Ask ChatGPT to create realistic sample records, customer profiles, transactions, survey responses, or other structured data. You can request CSV, JSON, or a spreadsheet-ready table. Best for: small datasets, prototypes, and testing. 2. Generate it with code Use Python libraries such as Faker for realistic names, addresses, and transactions, or SDV for learning patterns from existing tabular datasets. Best for: large datasets and repeatable generation. 3. Download an existing dataset Find synthetic datasets on platforms such as Kaggle or Hugging Face, or explore benchmark datasets released by research organizations. Best for: machine learning, experimentation, and research. 4. Use a specialized data-generation platform Tools such as Gretel, Mostly AI, and SDV can generate synthetic datasets based on schemas or real data, with capabilities varying by platform. Best for: business use cases and complex datasets. ## Which option should you use? | Your goal | Recommended starting point | | --- | --- | | Get data immediately | Ask ChatGPT to generate a CSV | | Generate thousands of fake records | [Faker](https://faker.readthedocs.io/en/stable/) | | Create realistic data based on an existing dataset | [SDV](https://docs.sdv.dev/sdv) | | Find datasets ready to download | [Kaggle](https://www.kaggle.com/datasets) | | Generate data for AI or machine learning | [Hugging Face Datasets](https://huggingface.co/datasets) | Faker generates individual synthetic values, while SDV can learn statistical patterns from existing data and generate new records. [Welcome to Faker’s documentation!](https://faker.readthedocs.io/en/stable/)[Welcome to the SDV!](https://docs.sdv.dev/sdv)[GitHub - sdv-dev/SDV: Synthetic data generation for tabular data](https://github.com/sdv-dev/SDV) ## Let me help you get the right data What kind of synthetic data are you looking for?

Synthetic data is artificially generated information that mimics the statistical properties of real-world data, and it’s increasingly used for AI training, software testing, and privacy-safe analytics. There are several ways to get synthetic data, depending on your goals: ## 🔑 Methods of Synthetic Data Generation * **Generative Adversarial Networks (GANs)** Two neural networks (generator \+ discriminator) compete, producing highly realistic data. Great for images, tabular data, and time-series. * **Variational Autoencoders (VAEs)** Encode data into a latent space and decode new samples. More stable than GANs, but lower peak quality. * **Diffusion Models** State-of-the-art for image generation, producing high-quality synthetic visuals. * **Rule-based / Statistical Methods** Generate data by sampling from distributions or applying explicit rules. Fast and predictable, but limited realism. * **LLM-based Generation** Large language models can produce synthetic text, dialogues, or structured datasets. * **3D Rendering & Simulation** Used for computer vision training, robotics, and autonomous driving (e.g., synthetic street scenes).[Synthetic Data Generation: Techniques, Tools, and Challenges in 2026 | Zylos Research](https://zylos.ai/research/2026-01-13-synthetic-data-generation/)[Synthetic Data Generation Methods: A Technical Guide for 2026 - Vivid3D](https://www.vivid3d.ai/blog/synthetic-data-generation-methods-a-technical-guide) ## 🛠 Tools You Can Use Here are categories of synthetic data tools: Tool Type| Examples| Best For ---|---|--- **Lightweight libraries**| Faker, dbldatagen| Unit tests, generating fake names, emails, IDs **Browser-based generators**| Mockaroo| Quick structured datasets without infrastructure **AI-native platforms**| Tonic.ai, Gretel, MOSTLY AI, SDV| High-fidelity synthetic data for ML training, compliance, enterprise-scale **Enterprise relational tools**| K2view| Complex databases with referential integrity **Healthcare/finance-specific generators**| Specialized platforms| Privacy-compliant synthetic patient or transaction data[Best Synthetic Data Tools Compared 2026 | Tonic.ai](https://www.tonic.ai/synthetic-data/best-synthetic-data-tools)[Best Synthetic Data Tools in 2026: A Developer's Honest Comparison · Misata Studio](https://www.misata.studio/blog/best-synthetic-data-tools-2026)[15 best synthetic data tools for AI and testing in 2026 - Guideflow Blog](https://www.guideflow.com/blog/synthetic-data-tools) ## ⚖️ Choosing the Right Approach * **For software testing** → Use Faker or Mockaroo for quick fake records. * **For AI/ML training** → Use GANs, diffusion models, or platforms like Gretel and Tonic.ai. * **For compliance/privacy** → MOSTLY AI or SDV, which preserve statistical fidelity while anonymizing sensitive data. * **For simulation-heavy tasks** → NVIDIA’s Cosmos Predict or 3D rendering engines for robotics and vision.[Synthetic Data Generation: Techniques, Tools, and Challenges in 2026 | Zylos Research](https://zylos.ai/research/2026-01-13-synthetic-data-generation/) 👉 Would you like me to walk you through **step-by-step how to generate synthetic data with a free tool (like Faker or Mockaroo)**, or are you more interested in **enterprise-grade AI platforms** that handle large-scale datasets?

Getting synthetic data - artificially generated data that mimics the statistical properties of real data without containing actual private or sensitive records - has become a standard practice for training machine learning models, testing software, and preserving privacy. Depending on your technical expertise, budget, and data type (tabular, text, images, time-series), there are several robust ways to acquire or generate it. ## 1\. Using Open-Source Python Libraries (Do-It-Yourself) If you have a seed dataset (real data) and want to generate more data that follows the same statistical distribution, open-source libraries are the most flexible and cost-effective approach. * **For Tabular Data (Rows and Columns):** * **SDV (Synthetic Data Vault):** One of the most popular libraries. It uses machine learning models (like Copulas, CTGAN, and TVAE) to learn patterns from a real table and generate new, privacy-safe rows. * **Gretel Synthetics:** Great for structured and time-series data, leveraging transformer models to generate realistic records. * **For Text and NLP:** * You can prompt Large Language Models (like Llama 3 or GPT-4o via API) to generate realistic synthetic text, customer reviews, logs, or question-answer pairs for fine-tuning. * **For Computer Vision (Images and Video):** * **Unreal Engine / Blender:** Used to render 3D scenes with randomized lighting, object placement, and textures to create perfectly labeled synthetic images (commonly used for autonomous driving or robotics). * **Diffusion Models:** Stable Diffusion can be fine-tuned or prompted to generate synthetic image datasets. ## 2\. Using Pre-Built Synthetic Datasets If you don't need a custom dataset modeled after your own proprietary data, you can download pre-made synthetic datasets for general testing and model prototyping. * **Kaggle:** Search for terms like "synthetic dataset" or "synthetic financial data." Many data scientists share open-source synthetic datasets specifically designed for fraud detection, healthcare, and customer churn. * **Hugging Face Datasets:** Home to thousands of datasets, including many instruction-tuning and text datasets generated entirely by LLMs (synthetic NLP data). ## 3\. Commercial Synthetic Data Platforms (Enterprise Solutions) If you are dealing with sensitive data (healthcare records, financial transactions, PII) and need enterprise-grade guarantees, compliance, and ease of use, commercial platforms offer managed solutions. * **Gretel.ai:** Offers APIs and tools for privacy-preserving synthetic data generation, differential privacy guarantees, and data validation. * **Hazy:** Specializes in synthetic data for financial services and banking, ensuring strict regulatory compliance (GDPR, CCPA). * **Mostly AI:** Focuses on high-fidelity synthetic tabular data, time-series, and relational data with automated quality checks. ## 4\. Rule-Based Generation (No Machine Learning) If you simply need mock data to test a database or application frontend (and don't need complex machine learning distributions), you can write scripts or use data generators: * **Faker (Python/JavaScript Library):** Instantly generates fake names, addresses, emails, credit card numbers, and phone numbers. * **Mockaroo:** A user-friendly web interface that lets you download custom CSV, JSON, or SQL datasets with fake names, dates, and custom formulas. ## Step-by-Step Workflow for Custom Synthetic Data If you are starting with a real dataset and want to build your own using SDV (Synthetic Data Vault): 1. **Install SDV:** Run `pip install sdv` in your Python environment. 2. **Load your real data:** Bring in your pandas DataFrame containing the sample data. 3. **Choose a Synthesizer:** Select an algorithm based on your data type (e.g., `CTGANSynthesizer` for complex tabular data with non-linear relationships). 4. **Fit the model:** Train the synthesizer on your real data (`synthesizer.fit(real_data)`). 5. **Sample new data:** Generate as many synthetic rows as you need (`synthetic_data = synthesizer.sample(num_rows=10000)`). 6. **Evaluate:** Check the statistical similarity and privacy scores to ensure the synthetic data is safe and accurate.

Getting synthetic data depends entirely on what kind of data you need (tabular, text, images, or relational databases) and your technical comfort level. The primary approaches and tools available to generate synthetic data are categorized below by use case: 1\. Programmatic & Open-Source Libraries (For Developers & Custom Needs) * * **Faker (Python / JS):** Great for generating individual mock data fields one-at-a-time (e.g., fake names, addresses, phone numbers, or credit card numbers) to populate databases or test user interfaces. [Best Synthetic Data Tools Compared 2026 | Tonic.ai](https://www.tonic.ai/synthetic-data/best-synthetic-data-tools)[Best Synthetic Data Tools in 2026: A Developer's Honest Comparison](https://www.misata.studio/blog/best-synthetic-data-tools-2026) * **Synthetic Data Vault (SDV):** A popular Python library that trains machine learning models on an existing real-world tabular dataset and samples new, statistically similar rows while maintaining privacy. [misata.studio] * **Large Language Models (LLMs):** You can prompt frontier models (like Claude or GPT-4o) to output customized structured JSON, text datasets, or edge-case training examples based on defined schemas and constraints. [Tonic.ai] * 2\. Specialized Platforms & No-Code Generators (For Enterprise & Mock Data) * * **Mockaroo:** A user-friendly web interface that lets you quickly build custom schemas and export thousands of rows of realistic mock tabular data in CSV, JSON, SQL, or Excel formats. [Tonic.ai] * **Tonic.ai (Fabricate / Structural):** An enterprise-grade platform that safely creates realistic synthetic databases mirroring your production database's structural integrity and statistical properties without risking real user PII (Personally Identifiable Information). [How to Generate Synthetic Data | Tonic.ai](https://www.tonic.ai/guides/how-to-generate-synthetic-data-a-comprehensive-guide) * **Gretel.ai & MOSTLY AI:** Advanced commercial platforms focused on privacy-safe synthetic data generation, transforming sensitive customer or patient data into high-utility, compliant synthetic equivalents. [misata.studio] * 3\. Computer Vision & Media Generation (For Images, Video, & 3D) * * **Datagen & Synthesis AI:** Specialized platforms that programmatically generate hyper-realistic, fully labeled 3D and 2D synthetic imagery of humans, objects, or driving scenarios - ideal for training computer vision and robotics models when real-world imagery is scarce or hard to capture safely. [misata.studio] * * * * Step-by-Chief-Step Approach to Get Started 1. **Define the Schema/Distribution:** Outline what fields, distributions, or statistical correlations your data needs to mimic. 2. **Choose Your Method:** Pick a simple library like `Faker` for basic mock testing, an ML tool like `SDV` if you have a seed dataset to copy, or an LLM prompt if you need conversational/text data. 3. **Validate the Output:** Check the generated data to ensure it meets your statistical requirements, doesn't accidentally leak real sensitive records, and successfully fulfills your downstream testing or training goals. [5 best practices for synthetic data use - IBM](https://www.ibm.com/think/insights/streamline-accelerate-ai-initiatives-synthetic-data) To help narrow down the best path forward, tell me: * What **type of data** are you trying to create (tabular/SQL, images, text, or user profiles)? * Is this for **software testing** , **machine learning training** , or **privacy compliance**?

You can get synthetic data by generating it yourself, using an open-source tool such as Synthetic Data Vault, or downloading a prebuilt dataset. If you want data tailored to your own records, use a sample dataset to train a generator, then check that the output is useful and doesn’t reveal sensitive information.([ibm.com][1])([mitsloan.mit.edu][2]) [1]: https://www.ibm.com/think/topics/synthetic-data "Types of synthetic data" [2]: https://mitsloan.mit.edu/ideas-made-to-matter/what-synthetic-data-and-how-can-it-help-you-competitively "What is synthetic data - and how can it help you competitively?"