Data Faker

THE PRACTICAL GUIDE · NORMANDIN SYSTEMS

Get more from Data Faker

Data Faker produces synthetic records from a JSON template using supported Faker methods and a chosen output format. A template makes test data predictable in structure while values can vary. The workspace includes a method picker, record count, locale selection, nested-object handling, and outputs including JSON, CSV, YAML, SQL, XML, and Markdown. Code transformations are most useful when you know the language and the surrounding context of the snippet. Work from a saved copy and start with a small, valid example that demonstrates the behavior you need. Formatting, encoding, and generation do not establish that a program is correct. Review the output as source code, keep meaningful comments when appropriate, and test the result in the same environment that will eventually interpret it. This makes subtle changes much easier to spot.

Guide updated October 13, 2026

How to use this tool

1. Paste a small, representative snippet and keep a copy of the input. Include the punctuation and delimiters needed to make the example meaningful.
2. Start with the supplied template or method picker, choose the number of records and output format, then generate a small sample.
3. Review the example below and compare its expected behavior with a small sample of your own input before continuing.
4. Copy the result into a scratch file and verify it with the intended parser, editor, or test environment before replacing the original code.

SEE IT IN PRACTICE

A worked example

A template containing a supported person-name method and a count of 3

Three records containing generated names

The exact values vary; the useful check is that each record follows the requested keys and value types.

Templates use method names as values

The supplied template demonstrates values such as faker.person.fullName and faker.internet.email, including a nested address object. The method picker helps find supported names without guessing. Literal values that are not Faker method expressions can remain fixed, making it possible to combine stable fixture fields with generated ones. The chosen locale affects available data conventions, but it does not prove the values satisfy an application's validation rules. Generate a few records first and inspect each field before increasing the count.

Errors can appear inside generated records

This implementation can return a method-resolution error as a generated value. That means a structured output may still contain an error string in one field instead of the intended synthetic data. Inspect values, not only whether the output parses. Check uniqueness, required formats, nested structure, and cross-field relationships explicitly. For CSV or SQL output, also review delimiters, quoting, and the target table or column assumptions. Synthetic records are useful test material, but realistic-looking names and addresses are not a substitute for carefully designed edge cases.

When this tool is useful

• Create sample records for interface development
• Prepare a small import test fixture
• Exercise optional fields without using real customer data

Tips for a better result

• Validate the generated structure, delimiters, date formats, and required fields before loading a larger fixture.
• Separate a syntax check from a behavior check. A snippet can parse successfully while producing the wrong value, selecting the wrong element, or changing an important edge case.
• Keep the source and the settings used for this result together. That makes a later comparison or correction easier, especially when another person needs to reproduce the same outcome.

Frequently asked questions