Databases & platforms — Test Data Management Guide

Fundamentals The foundations of test data management: terminology, the data lifecycle, and why teams adopt it in the first place.

Methods Approaches to provisioning safe, realistic test data: subsetting, masking, synthesis, and hybrid pipelines.

Compliance Keeping non-production environments compliant with GDPR, HIPAA, and PCI-DSS — without slowing engineering down.

Comparisons Evaluations of the platforms teams use for test data — Tonic Structural, Delphix, Redgate, and others.

Workflow How to implement test data management approaches within your existing tech stack and CI/CD pipeline.

Use cases What teams do with test data day to day, from QA automation and local development to demo environments and load testing, and what each job demands of the data.

Databases & platforms How test data management works in practice across the systems your data lives in, from PostgreSQL and Oracle to MongoDB and Snowflake.

Glossary Clear definitions of the terms that come up in test data work, from subsetting and referential integrity to format-preserving encryption.

Data masking The techniques for replacing sensitive values in non-production data, from redaction and tokenization to synthesis, and when each one is the right choice.

Test data management for Snowflake and Databricks