Fake Data Generator

Generate realistic fake people (name, email, phone, city) as JSON or CSV in one click.

Number of rows (1–100)

Output format

This is randomly generated, 100% fake placeholder data for testing and demos — it does not describe real people. Everything runs entirely in your browser; nothing is uploaded or stored.

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About the Fake Data Generator

A fake data generator builds realistic-looking but entirely invented records — names, emails, phone numbers, addresses, dates, UUIDs and more — so you can fill a database, test an API, or demo a UI without touching a single real person's information. Instead of hand-typing 'John Doe' a hundred times, you describe the shape of the data you need (a person field here, a city field there, a price column over there) and the tool produces as many rows as you ask for. The values look plausible because they're drawn from curated name lists and format rules, but none of them belong to anyone.

Reach for this tool whenever you need volume or variety that real data can't safely or quickly provide. Common moments: seeding a fresh dev database, populating a staging environment, building a prototype before the real data source exists, stress-testing pagination and search with thousands of rows, or creating screenshots and demos that won't leak customer details. It's also the cleanest way to produce edge cases on demand — unusually long names, accented characters, future-dated records — that rarely show up when you just copy a slice of production.

Under the hood the generator picks values per field according to its type. A 'full name' field combines a random first and last name; an 'email' field often derives from that name plus a placeholder domain; an 'address' bundles street, city, state and ZIP. You choose the row count and an output format — JSON for API mocking and front-end fixtures, CSV for spreadsheet and bulk database imports, or SQL INSERT statements to seed a table directly. Because the work happens in your browser, nothing you generate is uploaded anywhere.

One honest caveat: this data is realistic, not real. Generated emails are syntactically valid but not deliverable, phone numbers follow formatting patterns rather than connecting to a line, and credit-card-style numbers are made for format testing, not transactions. That's exactly the point — there is zero PII risk because none of it describes an actual human. If you need values that are internally consistent (a city matching its ZIP, for example) or that satisfy strict validators, check the output and tune your field choices before loading it into a system that enforces those rules.

Frequently asked questions

Is the data this tool creates safe to use, or does it contain real people's information?

It is completely safe — every value is randomly generated from name lists and format templates, so no record corresponds to a real person. Because the data was never real, there is no PII to leak, which is why synthetic data is the recommended way to populate dev and test environments.

Why shouldn't I just copy real customer data into my test environment instead?

Copying production data into non-production systems is a common GDPR and privacy risk: it spreads personal data to places with weaker access controls and no lawful basis for processing it. Generating fake data sidesteps the problem entirely, since there's nothing sensitive to mask, scrub, or accidentally expose.

Which output format should I pick — JSON, CSV, or SQL?

Use JSON for mocking APIs and front-end fixtures, CSV for spreadsheet work or bulk-importing into a database, and SQL INSERT statements when you want to seed a table directly. They contain the same rows — just packaged for different destinations.

Can I generate thousands of rows for load or performance testing?

Yes — generating large volumes is one of the main reasons to use a fake data generator, since you can't easily get that much real data and shouldn't risk it if you could. Just raise the row count; very large sets may take a moment because everything is built in your browser.

Are the generated emails and phone numbers real and usable?

No, and that's intentional. Emails are correctly formatted but undeliverable, and phone numbers follow valid patterns without connecting to any line, so they're ideal for testing form validation and display without contacting anyone by accident.

From our blog

From Raw Export to Clean Workbook: Converting CSV to Excel the Right Way

By the Super Simple Digital Tools Team · Updated June 2026

A CSV file is deceptively simple. It is nothing more than rows of plain text where commas mark the boundary between one value and the next. There are no fonts, no formulas, no column types, and no second sheet, just characters. That simplicity is exactly why nearly every database, e-commerce platform, and analytics tool can export one. But it also means a CSV carries no instructions about how its contents should be interpreted, which is where the trouble starts the moment you hand it to Excel.

Double-clicking a CSV asks Excel to guess. It guesses the delimiter based on your computer's regional list-separator setting, it guesses which columns are dates, and it guesses that long strings of digits are numbers it should round or abbreviate. When those guesses are wrong, a product code becomes scientific notation, a ZIP code loses its leading zero, '03/04' flips between March and April depending on locale, and a European semicolon file collapses into a single column. None of these are corruptions in the file; they are Excel's interpretation layered on top of it.

Converting the CSV into a proper .xlsx workbook first sidesteps most of the guessing. The conversion parses the text once, splits it on the real delimiter, honors quoted fields, and writes a structured workbook where each value already sits in its own cell. When the recipient opens that file, the column layout is fixed; it no longer depends on their regional settings or on Excel's import heuristics. You have effectively frozen the structure of the data before Excel gets a chance to reshape it.

The one area conversion alone cannot fully control is how Excel renders the numbers it sees. If a 16-digit shipment number is stored as a numeric value, Excel still applies its 15-digit precision limit and its scientific-notation display. The durable fix is to decide which columns are really identifiers, codes, or barcodes rather than quantities, and treat those as text. Text columns are displayed and stored verbatim, so an ID like 0080012345 keeps every character. This is a data-modeling choice, not a formatting afterthought.

For sensitive exports, where the conversion happens matters as much as how. Browser-based tools that run the parsing in JavaScript never transmit your file, so customer lists and financial extracts stay on your machine. Once you have your clean .xlsx, you get everything CSV cannot offer: formulas, multiple sheets, charts, conditional formatting, and pivot tables, all built on data that arrived in the right shape the first time.

Before converting, open the CSV in a plain text editor and glance at the first line to confirm whether it is comma-, semicolon-, or tab-separated so you know what to expect in the columns.
Treat ZIP codes, SKUs, phone numbers, and tracking IDs as text, not numbers, so Excel does not strip leading zeros or convert them to scientific notation.
If a value should stay in one cell but contains commas, make sure it is wrapped in double quotes in the source CSV; the converter keeps quoted fields, including "Last, First" names, together.
After opening the workbook, use Format Cells to set explicit number, date, or text formats per column, then save once as .xlsx so the formatting travels with the file.

Read the full guide →

Tool by the Super Simple Digital Tools Team. Reviewed by our editorial team. Free to use, no signup required.