Fresh test data on every pipeline run.
DataMaker generates and seeds realistic test data from a Python scenario or an MCP tool, no more shared staging, no more flaky tests, no more "works on my machine."
Pipelines stall on stale test data.
Stale shared staging
Tests pass yesterday and fail today because someone mutated the seed. There's no isolation per-PR.
Hand-maintained fixtures
JSON files in /tests drift from your schemas. A single migration breaks twenty test files at once.
Slow seed-and-teardown
Spinning a fresh DB and seeding it adds minutes to every run. Engineers wait. Cloud costs add up.
Generate, seed, and tear down per pipeline.
Author the data flow once. Run it from any CI step on any branch.
Author once, run anywhere
Write a Python scenario in DataMaker, version it, and run it from any CI step, GitHub Actions, GitLab CI, Jenkins, CircleCI.
AI agents in your pipeline
Wire DataMaker's MCP servers into your AI coding agent so integration tests can ask for the data they need on the fly.
Push to DB or API
Generated rows POST straight to your own REST or OData service, or land directly in PostgreSQL. MSSQL, Oracle and MySQL connect for schema reading and extraction.
One seed per PR
Each run gets its own seeded set so parallel jobs never trample each other and tests stay deterministic.
A scenario you can trigger from any CI step.
DataMaker scenarios are versioned, REST-triggerable Python scripts. Wire them into your pipeline and stream the logs back into your CI output.
- Versioned scenarios with API keys
- Live log streaming during execution
- Trigger via REST or MCP
- Retry-safe and idempotent
Stop maintaining seed scripts. Start running pipelines.
Start with a pilot on your own infrastructure, wired into one real pipeline.