Random Data Generator — 10 Field Types with CSV, JSON & SQL Export
Build structured test datasets without a login or account. Click to add fields from ten types — Full name, Email, Phone, Age, Date, City, Country, UUID, Boolean and Integer. Rename any field for your schema. Set the row count (up to 1,000) and preview the first 10 rows in a live table. Furthermore, export the full dataset as CSV, JSON or SQL INSERT statements instantly — making it the simplest mock data generator for developers and testers.
How to use the Random Data Generator
Click to add fields
Click any field type button — Full name, Email, Phone, Age, Date, City, Country, UUID, Boolean or Integer. Furthermore, each click adds the field to the schema below. You can add the same field type multiple times for multi-column schemas. Fields appear in the preview table in the order added.
Rename fields for your schema
Each field shows a name input next to its type badge. Furthermore, click the name and type your custom column name — "user_id", "customer_email", "registration_date" and so on. Renaming changes the column header in the preview table and the export file. Moreover, the field type (which determines the generated data) does not change when you rename.
Set the row count and generate
Enter how many rows to generate — from 1 to 1,000. Furthermore, the preview table always shows the first 10 rows. Click Generate Data to produce the dataset with fresh random values. The message bar confirms how many rows were generated if more than 10 were requested.
Review the preview table
The preview table shows up to 10 rows of the generated data. Furthermore, check that the column names match your expected schema and that the data types look correct. Adjust field order by removing and re-adding fields if needed. Click Generate Data again at any time to produce new values.
Export in your preferred format
Choose CSV for spreadsheet import, JSON for API or JavaScript use, or SQL INSERT for direct database seeding. Furthermore, click Export to download the full dataset — including all rows, not just the 10 shown in the preview. The SQL export creates a table named "random_data" with the field names as column names.
The ten field types and their generated values
Each field type generates realistic-looking values appropriate for its data type. Furthermore, understanding what each type generates helps build schemas that match real application data structures.
| Field type | Example value | Common use |
|---|---|---|
| Full name | Alice Smith | User display names, contact records |
| alice.smith42@gmail.com | User accounts, contact forms | |
| Phone | +4 392 4839201 | Contact records, SMS fields |
| Age | 34 | User profiles, demographic data |
| Date | 2018-07-23 | Registration dates, timestamps |
| City | Singapore | Address fields, location data |
| Country | Japan | Localisation, shipping data |
| UUID | 3f6a9b2c-1e4d-4a7f-b8c3-2d1e9f4a6b7c | Primary keys, session tokens |
| Boolean | true / false | Status flags, feature toggles |
| Integer | 742 | IDs, counts, scores |
How random test data improves software quality
Test data quality directly determines what bugs a test suite can find. Furthermore, deterministic test data — the same fixed records used every test run — only exercises code paths that work with those specific values. Random data exercises different paths on every run. Moreover, this probabilistic testing approach discovers edge cases and boundary conditions that fixed test data cannot reach.
UUID = crypto-random 128-bit value formatted as RFC 4122 v4
Date = uniform random day between 1 Jan 2010 and 31 Dec 2023
Integer = uniform random integer 0–999
Worked example: seeding a user database for a demo
A developer needs to seed a users table for a CRM demo with 50 realistic records. Schema: id (UUID), name, email, age, city, active (Boolean). Setup:
| Field type | Renamed to | Sample output |
|---|---|---|
| UUID | id | 3f6a9b2c-1e4d-4a7f-b8c3-2d1e9f4a6b7c |
| Full name | full_name | Carol Williams |
| carol.williams29@outlook.com | ||
| Age | age | 41 |
| City | city | Toronto |
| Boolean | is_active | true |
What is a random data generator?
A random data generator produces structured fake data for use in software development and testing. Furthermore, it generates plausible-looking values across multiple data types — names, emails, dates, identifiers — that match the structure of real application data without involving any actual users or sensitive information. Developers use it to seed databases, populate UI mockups and create test payloads for API testing. Moreover, the structured output — CSV, JSON or SQL — integrates directly with development workflows without manual formatting.
Random data generators evolved from manual test fixture creation — a time-consuming process where developers wrote custom scripts to insert test records. Furthermore, dedicated generators like Mockaroo became popular but require login and have usage limits. This LazyTools generator requires no account, no installation and no usage limits. Moreover, the field builder approach — click to add fields, rename, set row count — mirrors the mental model of defining a database schema without technical friction.
Test data in agile development
Agile teams need test data quickly — waiting for a DBA to generate test fixtures slows sprint velocity. Furthermore, a self-service generator like this one lets any team member create the test data they need immediately. QA engineers set up test scenarios without developer assistance. Designers populate prototypes with realistic content. Moreover, the SQL export connects directly to any relational database — paste the output into any SQL editor to seed the target schema instantly.
Why random data generators save development time
Manual test data creation is one of the most wasteful activities in software development. Furthermore, writing INSERT statements for dozens of realistic test records typically takes 15–30 minutes — for data that expires as soon as the database is reset. A random data generator produces 1,000 records in seconds. Moreover, the generated data is structurally identical to manually crafted fixtures but with far less effort and with random variation that increases test coverage.
GDPR compliance makes using production data in development environments legally problematic. Furthermore, copying real customer records to a development database exposes personal data to all developers with database access — a significant compliance risk. Randomly generated fake data provides the structural realism needed for development without creating any personal data liability. Moreover, regulators and auditors increasingly expect documented practices for keeping real personal data out of non-production environments.
JSON export for API testing
API testing tools like Postman, Insomnia and Bruno accept JSON payloads for automated test suites. Furthermore, generating 50 user records as JSON provides a ready-made dataset for testing pagination, search, filtering and sorting endpoints. Each JSON object matches the expected request or response schema exactly. Moreover, the UUID field generates primary key values that have no risk of conflicting with existing database records — an important consideration when testing create and update endpoints against a shared staging database.
Frequently asked questions
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