For CI/CD Teams

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."

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Why CI test data hurts

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.

How DataMaker fixes it

Generate, seed, and tear down per pipeline.

Author the data flow once. Run it from any CI step on any branch.

Python Scenarios

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.

MCP from CI

AI agents in your pipeline

Wire DataMaker's MCP server into your AI coding agent so integration tests can ask for the data they need on the fly.

Direct seeding

Push to DB or API

Generated rows go straight into Postgres, MySQL, Mongo, MSSQL, Oracle, DB2 — or POST to your own service.

Isolation

One seed per PR

Each run gets its own seeded set so parallel jobs never trample each other and tests stay deterministic.

Live scenario

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.

datamaker.automators.com · Scenarios · seed_orders.py Run

1from datamaker import DataMaker
2import requests
3
4dm = DataMaker()
5customers = dm.generate_from_id(
6    template_id="cust_de", count=500)
7
8for c in customers:
9    requests.post(
10        "https://staging.api/orders",
11        json={"customer": c["id"]})
12
13print(f"✓ seeded {len(customers)} orders")

Output (Live):25% height

→ Generating 500 customer records (template: cust_de)…→ POST https://staging.api/orders ✓ 200 OK ×500✓ seeded 500 orders

Versioned scenarios with API keys
Live log streaming during execution
Trigger via REST or MCP
Retry-safe and idempotent

We replaced our seed.sql with two DataMaker scenarios. CI runtime dropped from 14 to 4 minutes and our flaky-test rate fell to almost zero.

— DevOps Lead, EU SaaS startup

Stop maintaining seed scripts. Start running pipelines.

Start free, no credit card required — enough to wire DataMaker into a CI pipeline today.

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