Question 1

What is DataMaker and how does it work?

Accepted Answer

DataMaker is an AI-powered synthetic test data creation tool that helps software and QA engineering teams get production-ready test data on demand. The way it works is simple: you describe your data once (we call this a Template), and from then on you can generate that data on demand, in any volume, and push it directly into your databases, REST APIs, or SAP systems through saved Connections. For multi-step workflows, you can write a Python Scenario, and for anything you don't want to build manually, you can just describe it in plain English using the built-in AI Chat. All three layers, visual, scripted, and AI-driven, sit on the same engine, so nothing is locked into one mode. Book a demo to see how it works in action. You can also try our free test data generator in your browser with no signup.

Question 2

Why do I need DataMaker instead of just using a free fake data library or copying production data?

Accepted Answer

Both options sound easier than they are. Copying production data into test environments creates real legal risk. The moment real names, emails, or financial details land in a non-production system, you are potentially in breach of GDPR, HIPAA, or other privacy regulations. Free fake-data libraries avoid that risk but produce generic records that break relationships between tables, miss edge cases, and rarely produce country-specific formats like valid German IBANs or Austrian VAT IDs correctly. DataMaker sits between the two. It generates realistic, locale-aware synthetic data that is safe to use anywhere, supports complex enterprise scenarios like SAP business objects and CI/CD workflows, and handles sensitive data masking for regulated industries.

Question 3

What makes DataMaker different from other test data tools?

Accepted Answer

DataMaker ships with 50+ built-in data types, including locale-aware generators for IBANs, tax IDs, national IDs, phone numbers, and addresses in country-correct formats. Generated IBANs are checksum-valid; the other country formats match the documented shape of each country. You can also define custom types using AI prompts, Python, or regex, so almost no team hits a wall on field generation. Templates support nested objects, statistical distributions, ordered date sequences and status progressions, and a live preview that updates in real time. The bigger differentiators are native SAP OData integration, which makes us a listed SAP TDMS alternative, connectors for Tricentis Tosca and Jira/Xray, and a governed AI agent that runs multi-step jobs with roles, approvals, and a full audit trail.

Question 4

How does DataMaker integrate with my existing systems, databases, APIs, SAP, and CI/CD pipelines?

Accepted Answer

DataMaker connects to your systems through Connections, which you configure once and reuse everywhere. It supports PostgreSQL, Microsoft SQL Server, Oracle, and MySQL for reading schemas and extracting real rows, direct seeding into PostgreSQL, and any platform that exposes a REST or OData API. For SAP, DataMaker speaks OData natively across S/4HANA, RISE, and ECC environments, with service discovery from the Gateway catalogue, metadata parsing into a schema graph, and automatic CSRF token handling. It also connects to Tricentis Tosca, both Cloud and on-prem Commander, and to Jira Cloud with Xray. For CI/CD pipelines, Python Scenarios can be triggered via REST API from GitHub Actions, GitLab CI, Jenkins, and CircleCI. We also support AI coding assistants like Claude, Cursor, and GitHub Copilot through our MCP servers. Full technical details are in our API docs and product documentation . If you do not see your specific tool listed, please contact our team to confirm support.

Question 5

How does DataMaker keep my data private and GDPR-compliant?

Accepted Answer

In the enterprise setup, DataMaker runs inside your own network: the application, the credentials, and the generated data all stay behind your firewall. We do not need a copy of your data to make the product work. Templates default to Faker-based synthetic types; live data is opt-in via API/DB Response or chat retrieve. Synthetic E-Mail and IBAN field types produce structurally valid fake values. When you do work with real data, a Masking Policy assigns a strategy per field, replace with a realistic fake, hash, tokenise, redact, or preserve the format, across 29 recognised PII classes. Masking is deterministic and keyed on the kind of field rather than the column name, so a masked dataset still joins across tables. Tokenised values can be reversed only by someone holding a separate permission, and every reversal is recorded. Each masking run produces a report that records what was masked and how, and contains no raw values. Agent actions are written to an audit trail that lives in your own database.

Question 6

How Much Does DataMaker Cost, and How Do We Get Started?

Accepted Answer

Engagements start with a paid pilot on your own infrastructure, scoped to one real system so you can judge it against your own data. Pricing after that depends on your team size, the systems you connect to, and whether you need SAP-specific enablement. If you just want to see the generation engine first, our free test data generator runs in your browser with no signup. For specific pricing or a tailored session against your own scenario, book a pilot , use our contact page , or email office@automators.com .

Question 7

Where Can I Get Help or Support if I Have Questions About DataMaker?

Accepted Answer

For help or support, please visit our contact page or email office@automators.com . Product guides for Templates, Connections, Scenarios, and the AI Chat live at docs.datamaker.app , and the full REST API reference is at api.datamaker.automators.com . For technical or general queries, you can also connect with our CEO, Amin Chirazi , on LinkedIn.

Question 8

Do I Need Coding Skills to Use DataMaker, or Is It Beginner-Friendly?

Accepted Answer

DataMaker is beginner-friendly and does not require coding skills. The easiest entry point is the AI Chat, where you describe what you need in plain English. You can ask it to generate data, build a Template from an uploaded CSV or JSON sample, or pull real records from a connected system. The Template builder is also fully visual, so QA testers, business analysts, and product managers can define data structures without writing code. Developers can still go deeper through Scenarios, which give you a full Python environment with access to DataMaker's API. The same Template a non-coder built by clicking can be used inside a Scenario, so beginners and engineers collaborate on the same assets.

Question 9

How Quickly Can I Start Using DataMaker, and Is It Easy to Set Up?

Accepted Answer

You can try the generation engine instantly. Our free test data generator runs in the browser with no installation and no signup. For the full product, setup is typically a Connection to your target system, then either building a Template from scratch, picking a predefined one for common entities like Business Partners or Sales Orders, or uploading a CSV or JSON sample and letting the AI reverse-engineer a Template for you. Most teams have their first useful dataset flowing within an afternoon. SAP integrations can take slightly longer depending on landscape access. The full startup guide is in our documentation .

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