DataMaker Docs

Get started

Sign up, build your first template, and export your first dataset.

Templates

50+ field types, distributions, nested objects, and sensitive-data masking.

Connections

Push to Postgres / MySQL / Mongo / MSSQL / Oracle / DB2, REST endpoints, or SAP OData V2/V4.

Scenarios

Author Python scenarios, trigger them via REST or MCP, and stream live execution logs.

Agents & MCP

Drop the DataMaker MCP server into Claude, Cursor, or Copilot — 25+ tools for end-to-end test-data workflows.

Workflows

Seed CI/CD pipelines, pull SAP regression sets, mask PII for GDPR, or analyse existing data.

What DataMaker is

DataMaker is the agentic test-data platform from Automators. It speaks every shape your test data takes — SQL rows, REST payloads, SAP OData entities, nested JSON, locale-aware fakes, and PII-safe substitutions — and exposes it through a chat agent, a visual template builder, a Python scenario engine, and an MCP server for AI coding agents.

Use it to:

• Generate locale-correct, validated synthetic data (German USt-IDs that pass checksum, IBANs that pass MOD-97, IMEIs that pass Luhn).
• Fetch real records out of an existing system — typically SAP OData — for regression test sets that match the shape of a real test case.
• Seed databases, APIs, or SAP entities directly. No copy-paste, no seed.sql.
• Mask PII at the template level so the AI agent never exports sensitive fields by accident.

Where to go next

If you’ve never used DataMaker before, start here. It takes about five minutes to go from an empty workspace to a generated dataset, and another five to push that dataset into a sandbox.

If you’re integrating DataMaker into an existing pipeline, jump to Scenarios (Python triggered from CI) or MCP (your AI coding agent calling DataMaker tools).

If you’re replacing SAP TDMS or retiring a hand-rolled anonymiser, see Workflows → SAP regression and Workflows → Mask PII.