Generate Test Data Without an Account — Do It in Your Browser, Step by Step

You need 500 rows of realistic user records for a staging database, and the first thing most generators ask for is an email address. That's the moment you close the tab. Signing up to produce throwaway data is backwards — the data is fake, so why hand over something real?

This walkthrough covers the no-account route using Mock Data Generator, which runs faker.js locally in your browser. Nothing gets uploaded, nothing gets emailed to you, and there's no dashboard waiting on the other side of a confirmation link.

Why account-free matters more than it sounds

A mock data generator looks harmless. You're not uploading customer records, so what's the risk? The risk is the account itself. Sign-up walls usually mean your email, your IP, and a usage log live on someone else's server. For a solo developer or a contractor working under an NDA, that's a paper trail you didn't need to create.

There's also the friction tax. You want to seed a dev database at 11pm. A signup flow, a verification email, a free-tier row cap you discover after the fact — that's twenty minutes gone before you've generated a single row.

Browser-side generation removes both problems. The faker.js library loads into your page, you define fields, and the rows are built in memory on your machine. Close the tab and there's nothing left behind.

How to generate test data without an account in 5 steps

1. Open the tool and build a schema. Go to Mock Data Generator and add fields one at a time. Each field gets a name and a type — name, email, address, date, and the rest of the built-in set. This schema is the shape of every row you're about to produce, so match it to your actual table columns rather than inventing something close enough.

2. Pick types that match your real column types. A created_at column shouldn't get a random string. A price column shouldn't get a name. The free tier covers a limited set of field types, which handles the common cases: names, emails, addresses, dates, numbers, IDs. If your schema needs locale-specific data or more exotic types, that's where the paid tier comes in — but start free and see whether you actually hit the wall.

3. Set your row count. Free generation goes up to 20,000 rows. That's plenty for seeding a local Postgres instance or filling a paginated table so you can test scroll behavior. Type the number, don't guess at it — if you only need 200 rows for a unit test, generating 20,000 just makes your test file annoying to open.

4. Generate and preview. Hit generate and look at the output before you export. This is the step people skip, and it's the one that catches a swapped field or a date format that doesn't match your parser. Scan the first few rows. If email is showing addresses and phone is showing numbers, you're good.

5. Export as JSON or CSV. Download the file and drop it into your project. JSON if you're feeding a fixture loader or a mock API; CSV if you're importing into a spreadsheet or a SQL client's import wizard. Either way, the file lands in your downloads folder and never touches a server.

That's the whole loop. No email, no password, no "check your inbox to continue."

What the free tier actually gives you

Be clear-eyed about this before you commit a workflow to it.

• Up to 20,000 rows per generation on the free tier
• A limited set of field types — enough for names, emails, addresses, dates, and standard numeric fields
• JSON and CSV export
• No signup, no upload — faker.js runs in the page

The optional Pro tier is a one-time $14.99 and adds unlimited rows, 30+ field types including locale data, the ability to save up to 20 schemas in IndexedDB, and SQL INSERT plus multi-format ZIP export. If you're generating fixtures once a month, free is fine. If you're maintaining a dozen schemas across projects and want them to persist between sessions, the schema-saving alone earns its keep.

One thing worth knowing: saved schemas in the Pro tier live in IndexedDB, which is browser-local storage. They persist on that machine and that browser profile. They don't sync across devices, and clearing site data wipes them. Export anything you'd hate to rebuild.

The alternative: generating fake data from the command line

If you already live in a terminal, the obvious alternative is running faker directly. In Node, it's roughly:

npm install @faker-js/faker
node -e "const {faker}=require('@faker-js/faker'); \
for(let i=0;i<100;i++){console.log(JSON.stringify({\
  name: faker.person.fullName(),\n  email: faker.internet.email(),\n  city: faker.location.city()\n}))}" > seed.json

This is genuinely good. It's scriptable, it lives in your repo, and you can wire it into a seed script that runs on every npm run db:reset. The trade-offs are real, though: you need Node installed, you need to remember the faker API surface (it changed between major versions), and you need to write the loop yourself every time the schema shifts. For a one-off 300-row CSV to hand to a QA tester, that's more setup than the task deserves.

A reasonable split: use the browser tool when you want data now and don't want to think about it, and use a committed seed script when the fixture needs to be reproducible across the whole team. They're not competing approaches so much as different points on the same spectrum.

Edge cases worth planning for

Dates and timezones. Fake dates are usually generated in a default timezone. If your app stores UTC and renders local, test the boundary — a row generated at 2026-03-08T02:30:00Z will behave differently in a DST-observing locale. Generate a few rows around midnight and check how they render.

Uniqueness. Random generation doesn't guarantee unique values. If your schema has a username column with a unique constraint, a 20,000-row export will almost certainly contain collisions. Either add a numeric suffix to the field or plan to dedupe on import.

Referential integrity. Mock generators produce independent rows. They don't know that orders.user_id should point at a real row in users. If you need relational data, generate the parent table first, export the IDs, and use them when you build the child schema — or accept that you'll be writing a join script afterward.

Encoding and special characters. Names with accents, apostrophes, and non-Latin scripts are exactly what breaks a CSV import or an unescaped SQL string. If your generator can produce locale data, use it deliberately as a stress test rather than avoiding it.

A note on where the data lives

Everything described here happens inside the browser tab. There's no API call carrying your schema anywhere, and no file upload. The privacy posture is the same one behind the rest of the site's tools — you can read the specifics on the privacy page. If you also handle real user data in your day job and want to see how the same local-processing idea applies to image metadata, the EXIF Remover follows the same pattern.

For most people reading this, the practical takeaway is simple: the next time a generator asks you to create an account before it'll produce a single row, you have a browser tab that won't.

FAQ

Do I need to install anything to generate fake data in the browser?

No. The generator loads faker.js into the page itself, so there's no npm install, no Python environment, and no CLI setup. You open the tool, build a schema, and generate. If you later want a committed seed script for your repo, that's a separate decision — the browser route is for when you want rows immediately.

How many rows can I generate without paying?

The free tier supports up to 20,000 rows per generation, along with a limited set of field types and JSON or CSV export. That ceiling covers most local seeding and QA fixture work. The optional Pro tier raises it to unlimited rows and adds more field types, schema saving, and SQL INSERT or ZIP export.

Will my schema or generated data ever be sent to a server?

No. The generation runs locally in your browser, so your schema definition and the resulting rows stay on your machine. There's no upload step and no external API receiving your field names. You can verify the general approach on the site's privacy page.

Can I save a schema so I don't rebuild it every time?

Schema saving is part of the optional Pro tier, which stores up to 20 schemas in IndexedDB. Because that's browser-local storage, the schemas persist on that device and browser profile only — they don't sync elsewhere, and clearing site data removes them. Export anything you'd rather not recreate.

Is this a replacement for Mockaroo or a faker script?

It covers the same core job — schema in, realistic fake rows out — without an account or a server round trip. For one-off exports it's faster than writing a script. For fixtures that must be identical across a whole team on every run, a committed faker seed script in your repo is still the more reproducible choice.

Why do my generated rows sometimes have duplicate values?

Random generation doesn't enforce uniqueness, so columns like usernames or emails can collide once you're producing thousands of rows. If your target table has a unique constraint, add a numeric suffix to that field or plan to deduplicate during import. It's a known characteristic of random data, not a bug.