For most organizations, real data has become a liability. It’s fragmented, heavily regulated, and too sensitive for use in development at scale. Teams often spend weeks chasing approvals, only to end up with incomplete, redacted datasets that slow progress.
That’s why synthetic data generators are gaining ground. Instead of wrestling with real data, teams can now generate synthetic data based on real data structures to accelerate development while maintaining compliance. This approach is not only faster and safer, it’s quickly becoming standard. According to Gartner, 60% of data used in AI and analytics in 2025 will be synthetic.
But synthetic data is only as good as the generator behind it. This guide covers the top tools worth considering and how the best database IDEs can help query, validate, and integrate synthetic datasets across complex environments.
Let’s dive in!
- What is synthetic data?
- Key features of synthetic data generators
- Best synthetic data generators
- How dbForge Edge helps in synthetic data generation
- How to choose the right synthetic data generator
- Conclusion
- FAQ
What is synthetic data?
Synthetic data is artificially generated information that reflects the patterns and structure of real-world datasets, without including any actual records. It’s built from the ground up using various synthetic data generation methods, including simulations, statistical models, and neural networks such as GANs. The result behaves like real data but carries no privacy risks because it’s entirely fabricated.
For teams building AI and machine learning systems, that’s a game changer. You get control over the data—not just access. Want to stress-test an edge case that only happens once a year? Generate it. Need 10 million rows with the same schema but different distributions? Done.
That’s why creating synthetic data has become a core part of modern ML workflows. Let’s take a closer look at why it matters.
Why synthetic data is important
AI systems are only as good as the data they’re trained on — and most companies don’t have the luxury of perfect datasets. Worse, many can’t legally use the data they have.
That’s why synthetic data is gaining traction across industries. In healthcare, it allows teams to build models without touching patient records. In finance, it powers fraud detection without leaking sensitive transactions. In AI R&D, it fills in blind spots like rare edge cases, underrepresented classes, and data bias — which real-world logs often miss.
But here’s the real reason it matters: synthetic data breaks the bottleneck between compliance and speed. You can build faster without waiting on scrubbed datasets. You can ship safer without risking a privacy violation. And you can model the future without being chained to the past.
These advantages are prompting more teams to explore how to create synthetic data that simulates production-like conditions, without exposing real user data.
Key features of synthetic data generators
For those considering what synthetic data generation is or evaluating platforms to support ML projects at scale, understanding the most critical features is essential. Let’s break them down.
Data privacy and security
Privacy is the reason many organizations turn to synthetic data creation. But not all generators offer the same level of assurance.
High-quality synthetic data tools apply techniques like differential privacy, which adds statistical noise to prevent reverse engineering, and k-anonymity, which ensures each individual record is indistinguishable from at least k others. Some platforms also offer privacy risk scoring, helping teams identify fields that may pose re-identification risks.
These mechanisms are especially important in industries governed by GDPR, HIPAA, or PCI-DSS — where non-compliance can result in multimillion-dollar penalties. For example, healthcare tools like Synthea simulate patient records with built-in privacy protections, avoiding the use of any real PHI.
Data variety and realism
The effectiveness of synthetic dataset generation hinges on how well it captures the statistical and structural properties of actual datasets.
Advanced tools like SDV (Synthetic Data Vault) and CTGAN preserve multivariate distributions, inter-column dependencies, and rare class frequencies—factors that are vital for reliable model training. They support relational data synthesis, allowing for the creation of consistent, referentially intact datasets across multiple linked tables, which is crucial for simulating production database behavior.
Many generators also offer support for time-series synthesis, enabling the simulation of event logs, financial transactions, or sensor data with proper temporal coherence.
Customization and flexibility
Mature platforms allow users to define custom schemas, enforce field-level constraints, apply domain-specific rules, and even inject synthetic anomalies or edge cases. Some tools support Python SDKs or configuration-based workflows, enabling full scripting control for automation and repeatability.
These features are essential for teams learning how to generate synthetic data that fits complex requirements — not just structurally, but also behaviorally. For example, Faker offers fine-grained control over common data types and can be extended with custom providers or localized datasets. On the enterprise side, tools like K2View support advanced features like data masking, tokenization, and synthetic generation across distributed systems.
This level of flexibility is what allows synthetic data to move from R&D into production-grade workflows – powering QA, CI/CD pipelines, sandbox environments, and regulatory audits without compromising quality or speed.
Best synthetic data generators
From lightweight libraries for developers to enterprise-grade platforms with built-in compliance features, these are the best synthetic data generation tools to consider.
The top synthetic data tools
| Tool | Type | Key features | Best for | Pricing | Pros | Cons |
|---|---|---|---|---|---|---|
| K2View | Enterprise Solution | Large-scale data generation, privacy compliance, data integration, advanced analytics | Enterprises needing scalable synthetic data | Custom pricing | Supports complex data systems, high security | Expensive for small businesses |
| DataGen | Enterprise Solution | High-performance, customizable data generation, structured and unstructured data support | Computer vision, robotics, and simulation-heavy enterprise ML projects | Custom pricing | Tailored for enterprise needs, handles large datasets | Requires technical expertise for setup |
| Hazy | Enterprise Solution | AI-powered data generation, privacy-compliant, works with existing datasets | Businesses focused on privacy and security | Custom pricing | Strong privacy features, integrates with existing data | May require significant customization for use |
| Synthea | Open-Source (Healthcare) | Open-source tool for synthetic healthcare data generation, medical records simulation | Healthcare industry, researchers | Free | Trusted in healthcare, supports HIPAA-safe testing | Limited to healthcare-specific use cases |
| Faker | Open-Source (General) | Generates fake names, addresses, emails, and more; highly customizable | Developers needing flexible test data | Free | Fast, flexible, easy to integrate in dev pipelines | Not suitable for ML training or complex modeling |
| SDV (Synthetic Data Vault) | Open-Source (General) | Suite of tools for synthetic data modeling, works with tabular data, provides data correlation control | Data scientists needing advanced data models | Free | Customizable, supports multiple data formats | Steeper learning curve, requires technical expertise |
| CTGAN | Open-Source (General) | Uses GANs for generating realistic tabular data, accurate data distributions | Developers generating tabular data | Free | High accuracy in tabular data generation | Limited to tabular data, may not suit all use cases |
| Mostly AI | Specialized (Retail/Consumer Goods) | Synthetic data for retail, transaction data, customer behavior simulation | Retail, consumer goods industry | Custom pricing | Focused on retail, maintains data privacy | Primarily for retail, not suitable for other sectors |
| NDSI (National Data Science Institute) | Specialized (Finance) | Synthetic financial data, risk management, fraud detection datasets | Financial services, risk management | Custom pricing | Specialized for finance, regulatory-compliant | May not be flexible for non-financial use cases |
1. K2View
Company: K2View | Launch Year: 2009 | Pricing: Custom (enterprise licensing)
K2View is an enterprise-grade solution built for scale, combining synthetic data generation with deep data integration and privacy enforcement. It enables teams to generate synthetic datasets directly from live production schemas — preserving referential integrity while maintaining strict data governance.
The platform integrates smoothly into CI/CD pipelines, supports data masking and tokenization, and includes built-in compliance with regulations like GDPR and CCPA.
What sets K2View apart is its ability to generate synthetic data across systems, not just databases, but also APIs, flat files, and legacy platforms, making it ideal for large organizations that need secure and consistent test environments.
2. DataGen
Company: DataGen Technologies | Launch Year: 2018 | Pricing: Custom (based on project/volume)
DataGen is purpose-built for high-performance AI environments that demand realism, volume, and control — especially in computer vision and robotics. The platform specializes in generating 3D synthetic datasets, complete with tools for annotation, scene composition, and labeling, allowing teams to simulate rare or dangerous edge cases that are hard to capture in the real world.
While its core strength lies in vision-based data, DataGen also supports structured and unstructured formats for broader ML workflows. It’s a strong fit for industries like autonomous driving, industrial automation, and retail analytics that need scalable, simulation-based training data without relying on manual collection.
3. Hazy
Company: Hazy | Launch Year: 2017 | Pricing: Custom (enterprise plans only)
Hazy is a privacy-first synthetic data platform designed for highly regulated industries like banking, insurance, and telecom. It uses differential privacy and synthetic modeling to generate data that retains statistical fidelity without ever referencing real customer records — helping teams meet strict legal and compliance standards.
Hazy supports both tabular and relational data generation, with fine-grained controls to tune distributions, apply business logic, and enforce policy rules during the creation process. With a scalable, API-first architecture, it’s a strong fit for enterprises where data privacy, control, and auditability are non-negotiable.
4. Synthea
Company: The MITRE Corporation | Launch Year: 2016 | Pricing: Free (open-source)
Synthea is a leading open-source tool for generating realistic synthetic patient records — widely used in medical research, public health simulations, and EHR testing. It simulates clinical encounters, prescriptions, demographic profiles, and disease progression using healthcare standards like HL7 FHIR and ICD-10.
Because it’s rule-based and entirely open-source, Synthea enables researchers and developers to generate millions of lifelike patient journeys without risking HIPAA violations or relying on actual health data. If your work involves healthcare datasets and you need realism without regulatory friction, Synthea remains one of the most trusted solutions available.
5. Faker
Company: Open-source community | Launch Year: 2011 (Python); JS version 2021 | Pricing: Free (MIT License)
Faker is a lightweight Python library for generating mock data, ideal for developers working on QA, testing, and prototyping tasks. It can generate a wide range of localized values, including names, addresses, timestamps, currencies, and more. With support for custom providers and integration into test frameworks, Faker is a fast and flexible way to create repeatable test datasets.
While it’s not designed for training machine learning models or use in privacy-sensitive environments, Faker remains a reliable go-to for synthetic test data generation in modern dev stacks where speed and flexibility matter.
6. SDV (Synthetic Data Vault)
Company: Data to AI Lab at MIT | Launch Year: 2018 | Pricing: Free (open-source); Enterprise options via Gretel.ai and others
SDV is one of the most widely used open source synthetic data generation tools. It enables users to create high-quality synthetic datasets that preserve the statistical properties and relationships found in real-world tabular and relational data.
SDV includes a suite of models such as TVAE (Tabular Variational Autoencoder) and GaussianCopula, designed to handle both categorical and continuous variables. It also supports multi-table relational modeling, making it ideal for generating complex, referentially consistent datasets for sandbox environments or machine learning workflows.
With built-in features for data evaluation, model tuning, and reproducibility, SDV is a top choice for data scientists and engineers who need transparency and control when generating synthetic data.
7. CTGAN (Conditional GAN)
Company: Data to AI Lab at MIT (part of SDV ecosystem) | Launch Year: 2019 | Pricing: Free (open-source)
CTGAN is a deep learning model purpose-built for synthetic tabular data generation. It extends the standard GAN framework to better handle imbalanced distributions, mixed data types, and discrete values.
By learning from the conditional distributions within the training data, CTGAN produces statistically consistent samples that closely match the original data’s behavior. This makes it a strong choice for machine learning teams working on classification or regression tasks where real data is limited, sensitive, or non-shareable.
As part of the SDV ecosystem, CTGAN is particularly useful when high-fidelity synthetic data is needed for privacy-safe model development or analytics in structured business domains.
Mostly AI
Company: Mostly AI | Launch Year: 2017 | Pricing: Custom (enterprise licensing)
Mostly AI is a synthetic data platform optimized for customer-centric use cases like retail, banking, and insurance. It generates high-quality, privacy-preserving synthetic datasets that simulate transactional behavior, customer profiles, and purchasing patterns — all while maintaining GDPR and enterprise-level compliance.
Mostly AI’s strength lies in its ability to produce structured behavioral data at scale, enabling teams to train models or run analytics without exposing sensitive customer information. Its focus on realistic data distributions and attribute correlation makes it a powerful tool for organizations handling personal data in high-volume, regulated environments.
How dbForge Edge helps in synthetic data generation
While synthetic data is typically generated with dedicated tools, using it effectively requires the right database infrastructure. dbForge Edge brings test data generation and multi-database management into a single, unified IDE—helping teams operationalize synthetic data across SQL Server, MySQL, PostgreSQL, and Oracle. Let’s get into detail.
Feature-rich test data generation
dbForge Edge includes a Test Data Generator, powered by the same engine as the standalone dbForge Data Generator for SQL Server. This means users benefit from:
- Multiple built-in generators for values like names, addresses, emails, credit cards, etc.
- Pattern-based generation using regex, custom masks, and weighted lists.
- Support for complex data types such as GEOGRAPHY, XML, and HIERARCHYID.
- Referential integrity preservation across foreign keys and relationships.
- Custom generators and reusable templates for domain-specific or localized data.
- Automation-ready generation via CLI and .bat scripts—ideal for DevOps and CI/CD pipelines.
These capabilities allow teams to simulate realistic production-like environments without relying on real data or manually crafting records.
Real benefits for development and compliance
- Speed up development cycles by automating the creation of lifelike test data.
- Enable ML model training and testing with safe, production-like datasets.
- Enhance QA environments with realistic, interdependent data tables.
- Ensure privacy compliance by replacing real records with synthetic alternatives.
- Support audit and regulatory scenarios with controllable, non-sensitive data.
Use cases and application scenarios
dbForge Edge plays a vital role in multiple synthetic data scenarios:
- Automate fresh test data creation for every build or release.
- Generate large-scale, statistically rich datasets for training models.
- Provide synthetic data in finance, healthcare, and telecom where compliance (e.g., GDPR, HIPAA) is non-negotiable.
- Simulate realistic workflows without production data exposure.
- Use entirely fake but structurally valid data instead of redacting real datasets.
In short, dbForge Edge turns synthetic data into a practical asset—ready for testing, compliance, and real-world workflows.
How to choose the right synthetic data generator
Selecting the right synthetic data tool means aligning it with your data structure, compliance needs, and long-term goals. Here’s what to prioritize:
- Privacy and Compliance: In regulated industries, look for synthetic data generation tools with differential privacy, risk scoring, and policy-based controls to ensure compliance with laws like GDPR or HIPAA. Platforms like Hazy are purpose-built for these requirements.
- Scalability and Integration: Choose a generator that supports large-scale data creation, integrates with CI/CD pipelines, and works across your existing databases. Tools like K2View are built for high-volume, enterprise-grade environments.
- Cost vs. Value: Balance features against budget. While paid tools offer advanced support and functionality, open-source options like Faker, Synthea, and SDV deliver strong capabilities for teams with simpler needs or limited resources.
- Strategic Fit: Ensure the tool aligns with your broader goals—whether that’s accelerating AI, enabling safe testing, or meeting compliance at scale. The right choice should enhance your workflow, not slow it down.
Conclusion
Synthetic data is no longer a secondary option — it’s becoming the backbone of secure, scalable data operations. It enables teams to test, train, and deploy faster, without relying on sensitive production data or navigating legal bottlenecks that slow development cycles.
But generating synthetic data is only half the equation. To make it usable, you need tools that can integrate it across databases, enforce security protocols, and validate its structure before it reaches downstream systems. Tools like dbForge Edge fill that gap by combining advanced test data generation with multi-database integration, security, and automation—giving teams full control to manage and deploy synthetic datasets confidently at scale.
Looking ahead, teams that invest early in the right synthetic data tools won’t just stay compliant, they will generate synthetic data faster, smarter, and safer across their workflows.
FAQ
How can synthetic data generators help ensure privacy and compliance?
Synthetic data generators create artificial datasets that replicate the statistical properties of real data without exposing actual records. Tools with built-in privacy techniques—like differential privacy or k-anonymity—help ensure compliance with data protection laws such as GDPR, HIPAA, and CCPA.
Which open-source tools are best for generating synthetic data?
Open-source tools like SDV, Synthea, and Faker are widely used. SDV is ideal for tabular and relational data, Synthea specializes in healthcare datasets, and Faker is great for lightweight testing and mock data generation.
How do synthetic data generators improve machine learning model training?
By providing clean, balanced, and customizable datasets, synthetic data helps improve model generalization and performance—especially when real data is limited, biased, or heavily regulated. It also allows safe simulation of rare or edge-case scenarios.
What are the pros and cons of using synthetic data in various industries?
Pros include improved data privacy, faster model development, and safer testing environments. Cons can include lower accuracy if the synthetic data isn’t realistic enough, and added complexity when integrating into regulated workflows.
