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Seamless integrations for your enterprise data workflows

Our products offer performant, native integrations with structured, semi-structured, and unstructured data sources, from relational databases to cloud-based data warehouses to files and NoSQL data stores.

Guides

Explore the world of data synthesis and discover how it plays a crucial role in safeguarding sensitive information while maintaining data utility in software and AI development.

Test Data Management

Managing test data from multiple sources without losing consistency

Test Data Management

Test data subsetting strategies for targeted software testing

Tonic Fabricate how-tos

Creating unstructured files from Fabricate Data Agent generated data

Tonic Fabricate how-tos

Using real-world data for synthetic data generation with the Fabricate Data Agent

Data synthesis

Synthetic data for agentic workflows: A guide

Data privacy in AI

Named Entity Recognition for data compliance automation

Developer productivity

How to hydrate agile development environments with realistic test data

Developer productivity

Build vs buy: Your guide to scalable synthetic data via LLMs

Data synthesis

How to generate synthetic data via agentic AI

Data synthesis

What is Synthetic Data?

Tonic Structural how-tos

How to use Structural data and Claude Code for test automation

Test Data Management

How to ensure test coverage for edge cases with representative data

AI model training

How to develop AI training datasets for compliance and performance

Data synthesis

Data synthesis for AI: A privacy-first approach

AI model training

Secure data generation for AI model training

Data privacy in AI

Preventing data breaches in AI systems

AI model training

How to prepare machine learning data responsibly

Data privacy in AI

Data masking and artificial intelligence: Protecting data

Test Data Management

Masking and subsetting data to optimize test data pipelines

Data synthesis

Data synthesis vs data masking

Data synthesis

Data synthesis techniques: a comparison for developers

Developer productivity

How to improve data accessibility for software and AI development

Data de-identification

Deterministic masking, explained

Tonic Fabricate how-tos

Managing access to Tonic Fabricate accounts and workspaces

Data privacy in AI

PII compliance checklist: How to protect private data

Data de-identification

Real-world applications of format preserving encryption

Data de-identification

Data masking for the insurance industry: A guide

Data synthesis

What is a rule-based test data generator?

Test Data Management

Data masking’s role in leveraging production data for testing and development

Tonic Fabricate how-tos

Uploading and referencing production data in a rule-based dataset, with Tonic Fabricate

Data de-identification

Data masking for government agencies: A guide

Tonic Structural how-tos

How to mask data in Snowflake: A step-by-step guide

Developer productivity

Build vs buy: Your guide to finding scalable, efficient test data solutions

Test Data Management

Questions to ask when selecting a Test Data Management service

Developer productivity

Data in action: How quality data can revolutionize the financial industry

Data privacy in AI

AI in healthcare: Data privacy and ethics concerns

Test Data Management

How to gather test data for testing purposes: a guide

Developer productivity

Data in action: How quality data can transform the healthcare industry

Developer productivity

How data quality issues can slow down product development

Developer productivity

How better data helps you do more

AI model training

A comprehensive guide to ethical fine-tuning of Large Language Models

Data privacy in AI

Privacy by Design in generative AI: Building secure and trustworthy AI systems

Tonic Structural how-tos

Creating an enterprise test data strategy with Tonic Structural

AI model training

Balancing compliance and data utility in AI model training

Tonic Structural how-tos

Integrating Tonic Structural with your existing tech stack

Test Data Management

How to overcome common data provisioning challenges

Data privacy in AI

AI & data privacy: What every organization needs to know

Data de-identification

Use cases for de-identified datasets

AI model training

Synthesizing healthcare data for AI model training, with HIPAA Expert Determination

Data privacy in AI

Data privacy vs security: Understanding the difference

Data privacy in AI

AI compliance tools for your business

Test Data Management

Unstructured data management: What it is and how to manage it

AI model training

What is a RAG chatbot? Benefits, challenges, and how to build one

Data privacy in AI

Best LLM security tools: Features & more

Data privacy in AI

Understanding LLM data security risks (with solutions)

Data de-identification

Understanding data redaction: use cases, benefits, and how to automate redaction workflows

Test Data Management

The hidden value of test data: a case study on tech debt & business value

Data de-identification

Data anonymization vs data masking: is there a difference?

Data de-identification

Data de-identification in the healthcare industry

Data de-identification

Static vs dynamic data masking

Data de-identification

Data anonymization: a guide for developers

Tonic Textual how-tos

De-identifying your unstructured data in Databricks with Tonic Textual

Data synthesis

How to generate synthetic data: a comprehensive guide

Data de-identification

Data de-identification in the finance industry

Tonic Structural how-tos

Custom sensitivity rules to automate sensitive data detection

AI model training

What is retrieval augmented generation? The benefits of implementing RAG in using LLMs

Tonic Textual how-tos

Using custom models in Tonic Textual to redact sensitive values in free-text files

Tonic Textual how-tos

How to prevent data leakage in your AI applications with Tonic Textual and Snowpark Container Services

Tonic Textual how-tos

How to automatically redact sensitive text data In JSON format

Data privacy in AI

Safeguarding data privacy while using LLMs

AI model training

Top 5 trends in enterprise RAG

Tonic Structural how-tos

Ensuring data privacy with privacy rankings in Tonic Structural

Data synthesis

Guide to data privacy compliance for financial institutions

Tonic Structural how-tos

Security for Tonic.ai cloud products

AI model training

What is model hallucination?

AI model training

What is Named Entity Recognition (NER)?

Data de-identification

What is data de-identification?

Data privacy in AI

Understanding model memorization in machine learning

Tonic Structural how-tos

Using Tonic Structural and the Safe Harbor method to de-identify PHI

Tonic Structural how-tos

Maintaining data relationships in Structural generation output

Tonic Structural how-tos

Integrating Tonic Structural into your data refresh and CI/CD pipelines

Test Data Management

Guide to test data automation

Test Data Management

Tonic vs Delphix vs K2View vs IBM Optim. A full comparison.

Test Data Management

What is test data management? A guide to TDM solutions

Data de-identification

What is data masking?

Data de-identification

Data masking vs data tokenization: differences and use cases

Data de-identification

What is data obfuscation?

Build better and faster with quality test data today.

Unblock data access, turbocharge development, and respect data privacy as a human right.