How does deterministic seed generation work for CI/CD test fixtures?

When you provide an integer seed (e.g., seed: 42), the underlying pseudorandom number generator (PRNG) is initialized with that fixed state. Every subsequent run with the identical configuration produces the exact same sequence of records. This allows frontend and QA teams to maintain deterministic snapshot fixtures in automated Vitest, Jest, or Playwright pipelines without checking in multi-megabyte static JSON files.

How are JSON Schema format hints mapped to realistic fake data?

The generator inspects property annotations defined in Draft-07 or Draft 2020-12 schemas. Keywords like "format": "email", "format": "uuid", "format": "uri", "format": "date-time", "format": "ipv4", and "format": "ipv6" are mapped to specialized generators rather than generic strings. Additionally, enum arrays are sampled randomly, and min/max numeric constraints shape realistic number ranges.

How do I integrate mock datasets with Mock Service Worker (MSW 2.0)?

Generate your desired record count (e.g., 20 users), copy the JSON output or fetch it directly via the REST API endpoint (/api/tools/mock-data-generator), and return it within an MSW http.get("/api/users", () => HttpResponse.json(mockUsers)) handler. This allows UI components to exercise pagination, filtering, and sorting against realistic payload structures.

How can I simulate edge cases such as missing values or localized formatting?

Enable the "Include nulls" option and adjust the null probability slider (0–50%) to stress-test your frontend null-safety guards and TypeScript optional types. To verify internationalization, switch between the 9 available locales (such as Japanese, German, or Brazilian Portuguese) to generate scripts and telephone formats that reflect global customer data.

What is the difference between mock data and synthetic data?

Mock data consists of randomized fake records structured for development, UI wireframing, and contract unit tests without requiring statistical parity. Synthetic data algorithms generate privacy-preserving datasets that mirror the exact statistical distributions and correlation matrices of production tables, typically for ML training and analytical modeling.

How does the "Infer from Sample" mode work?

Paste any representative JSON payload into the sample area. The inference engine performs pattern analysis on each property: RFC-compliant email expressions, UUID v4 strings, ISO timestamps, URL protocols, and boolean flags are recognized and converted into reproducible field generators that mirror your original data model.

Mock Data Generator — Free Fake JSON & Schema Data

DevFlow

Free online mock data generator for developers, QA testers, and designers. Generate realistic fake data from JSON Schema definitions, custom field templates, or by inferring types from a sample JSON object. Choose from 30+ field types including first names, last names, emails, phone numbers, street addresses, cities, countries, UUIDs, dates, timestamps, company names, job titles, URLs, IP addresses, credit card numbers, and more. Support for 9 locales including English, German, French, Spanish, Italian, Portuguese, Japanese, Korean, and Chinese for region-specific data. Generate 1 to 10,000 records with optional null values and reproducible output via seed. Built-in presets for Users, Products, Orders, and Posts get you started in one click. Output is formatted JSON ready for test fixtures, API mocking, database seeding, and UI prototyping. Runs entirely in your browser — no data is ever sent to a server. Free REST API available for programmatic access.

JavaScript is required to use this interactive tool. Please enable JavaScript in your browser, or use our Mock Data Generator API for programmatic access.

Browse all Generators tools →

Popular Generators tools

Browse all 5 Generators tools →

Mock Data Generator is also known as: Fake JSON Generator, JSON Schema Mock Data Generator, Faker Online, Test Data Generator, Dummy JSON Generator, Sample Data Generator.

How to Mock Data Generator Online

Choose your input mode — Field Builder, JSON Schema, or Infer from Sample — using the mode selector on the left panel.
In Field Builder mode, add fields by name and select a data type from 30+ options including names, emails, addresses, UUIDs, dates, and more. Or load a preset (Users, Products, Orders, Posts) for instant setup.
In JSON Schema mode, paste a JSON Schema definition and the tool automatically maps schema types and format hints to realistic fake data.
In Infer from Sample mode, paste a sample JSON object or array and the tool infers field types from the data patterns — strings that look like emails become emails, UUID-shaped strings become UUIDs, and so on.
Set the record count (1–10,000) using the slider or input field. Choose a locale for region-specific names, addresses, and phone numbers.
Optionally configure a seed value for reproducible output across runs, and toggle null values with adjustable probability.
Click Generate or press ⌘↵ to create your mock data. Output appears as formatted JSON instantly.
Copy the generated JSON with ⌘⇧C, download as a .json file with ⌘⇧D, or share your configuration via URL parameters.

Mock Data Generator Features

3 input modes: Field Builder for explicit control, JSON Schema for schema-driven generation, and Infer from Sample to auto-detect field types from example data.
30+ realistic data types: first name, last name, full name, email, phone number, street address, city, country, company name, job title, URL, IPv4, IPv6, UUID, date, timestamp, number, float, boolean, word, sentence, paragraph, color, hex color, emoji, avatar URL, password, credit card number, and bank account IBAN.
9 locale support: generate region-specific data for English (US), German, French, Spanish, Italian, Portuguese (Brazil), Japanese, Korean, and Chinese (Simplified) — with localized names, addresses, and phone formats.
4 built-in presets: instantly load field templates for Users, Products, Orders, and Posts to get started in one click.
Reproducible output: set a seed value to generate identical data across multiple runs — ideal for consistent test fixtures and database seeding.
Nullable field support: optionally include null values in your output with configurable probability (0–50%) to test null-handling logic.
JSON Schema intelligence: automatically maps JSON Schema types and format hints (email, uri, uuid, date-time, ipv4, ipv6) to appropriate faker types, and handles enum values.
Smart type inference from sample JSON: detects email patterns, UUID shapes, URL prefixes, date formats, numeric types, and more from your sample data.
Bulk generation: create up to 10,000 records in a single run with real-time performance stats showing record count, field count, output size, and generation time.
Shareable URLs: all settings are encoded in URL parameters so you can bookmark or share your exact configuration with teammates.
Works entirely in your browser — your schemas, samples, and generated data never leave your machine. No signup required.
Keyboard shortcuts for power users: ⌘↵ to generate, ⌘⇧C to copy, ⌘⇧D to download, ⌘⇧K to clear.
REST API available at /api/tools/mock-data-generator for programmatic access — supports GET with query parameters and POST with JSON body.

Supported Formats & Dialects

The Mock Data Generator supports 5 syntax formats and dialects for accurate parsing and processing.

Field Builder Mode: Granular field-level generation supporting 30+ Faker.js types across Person, Address, Company, Web, ID, DateTime, Finance, and Color categories.
JSON Schema Mode: Automated synthetic generation from JSON Schema Draft-04, Draft-07, and Draft 2020-12 specifications, parsing string format keywords (email, uuid, uri, date-time, ipv4, ipv6).
Sample Data Inference: Reverse-engineers existing JSON objects or arrays using heuristic pattern recognition (UUID regex, ISO 8601 dates, email formats, and number bounds).
Multi-Locale Synthetics: Region-aware localization across 9 locales (EN, DE, FR, ES, IT, PT-BR, JA, KO, ZH-CN) for realistic phone number formatting, street addresses, and geographic names.
Deterministic PRNG Seeding: Pseudo-Random Number Generator (PRNG) seeding allowing repeatable, immutable synthetic datasets across CI test suites and staging fixtures.

All Guides

generators

Generating Realistic Mock Data for APIs: MSW, Faker, and JSON Schemas

Master synthetic mock data generation for APIs: build realistic datasets using Faker, JSON Schema, and Mock Service Worker (MSW 2.0) for frontend testing and prototyping.

Read Field Manual

generators

UUIDv7 vs ULID vs UUIDv4: Database Primary Key Performance Guide

Compare RFC 9562 UUIDv7, ULID, and UUIDv4 for database primary keys. Understand B-tree index fragmentation, sortability, and storage efficiency in PostgreSQL and MySQL.

Read Field Manual

developer tools

Visualizing and Debugging Complex JSON Schemas: $defs, Recursion, and OpenAPI 3.1

Master JSON Schema visualization and debugging: resolve complex $defs pointers, catch recursive circular loops, inspect validation constraints, and render OpenAPI diagrams.

Read Field Manual

All Standards

JSON Schema (Draft 2020-12)Data Formats

JSON Schema is a declarative JSON dialect for annotating, validating, and establishing structural contracts for JSON documents and REST APIs.

Lorem Ipsum (Placeholder Dummy Text)Developer Tools

Lorem Ipsum is scrambled Latin placeholder text used in typography, graphic design, and web development to preview layouts before final copy is available.

AI Model Token Pricing

Explore Full AI Model Pricing Directory

Compare per-token rates, prompt caching discounts, and context windows across leading LLMs (GPT-4o, Claude 3.5 Sonnet, Gemini 2.5 Flash, DeepSeek, and more) in our verified catalog.

Browse AI Models Directory Sonnet vs GPT-4o Cost Benchmark

Pre-built Automation Pipelines

Chain Mock Data Generator with other utilities in a multi-step visual workflow.

beginner

encoding

Base64 Decode → JSON Format

Decode a Base64 string and pretty-print the JSON inside it.

Flow:

InputBase64 Encode/DecodeJSON FormatterOutput

4 nodes

beginner

data-transformation

CSV → JSON → YAML

Convert CSV data to JSON, then to YAML format.

Flow:

InputCSV to JSONYAML ConverterOutput

4 nodes

intermediate

development

JSON Format → TypeScript Schema

Format JSON and generate TypeScript/Zod schema from it.

Flow:

InputJSON FormatterJSON to TypeScript & Schema GeneratorOutput

4 nodes

Frequently Asked Questions

How does deterministic seed generation work for CI/CD test fixtures?: When you provide an integer seed (e.g., seed: 42), the underlying pseudorandom number generator (PRNG) is initialized with that fixed state. Every subsequent run with the identical configuration produces the exact same sequence of records. This allows frontend and QA teams to maintain deterministic snapshot fixtures in automated Vitest, Jest, or Playwright pipelines without checking in multi-megabyte static JSON files.
How are JSON Schema format hints mapped to realistic fake data?: The generator inspects property annotations defined in Draft-07 or Draft 2020-12 schemas. Keywords like "format": "email", "format": "uuid", "format": "uri", "format": "date-time", "format": "ipv4", and "format": "ipv6" are mapped to specialized generators rather than generic strings. Additionally, enum arrays are sampled randomly, and min/max numeric constraints shape realistic number ranges.
How do I integrate mock datasets with Mock Service Worker (MSW 2.0)?: Generate your desired record count (e.g., 20 users), copy the JSON output or fetch it directly via the REST API endpoint (/api/tools/mock-data-generator), and return it within an MSW http.get("/api/users", () => HttpResponse.json(mockUsers)) handler. This allows UI components to exercise pagination, filtering, and sorting against realistic payload structures.
How can I simulate edge cases such as missing values or localized formatting?: Enable the "Include nulls" option and adjust the null probability slider (0–50%) to stress-test your frontend null-safety guards and TypeScript optional types. To verify internationalization, switch between the 9 available locales (such as Japanese, German, or Brazilian Portuguese) to generate scripts and telephone formats that reflect global customer data.
What is the difference between mock data and synthetic data?: Mock data consists of randomized fake records structured for development, UI wireframing, and contract unit tests without requiring statistical parity. Synthetic data algorithms generate privacy-preserving datasets that mirror the exact statistical distributions and correlation matrices of production tables, typically for ML training and analytical modeling.
How does the "Infer from Sample" mode work?: Paste any representative JSON payload into the sample area. The inference engine performs pattern analysis on each property: RFC-compliant email expressions, UUID v4 strings, ISO timestamps, URL protocols, and boolean flags are recognized and converted into reproducible field generators that mirror your original data model.

Developer Reference & Learning Hubs

Developer Guides

In-depth technical tutorials, debugging guides, and best practices.

Technical Glossary

Authoritative definitions for RFC standards, formats, and protocols.

AI Models Directory

Verified per-token API pricing, rate cards, and benchmarks across LLMs.

Mock Data Generator — Free Fake JSON & Schema Data