How It Works

You provide a target table name and a list of columns, one per line. Each line describes the column name and a data type hint. The tool uses the type hint to create safe, SQL-escaped dummy values and then assembles them into INSERT statements in the dialect you choose.

Input format

One column per line using column_name:type (example: email:email).
Optional length for text types like varchar(80) to keep values within limits.
Optional enum values using enum(a,b,c) to generate valid, constraint-friendly data.
Common types supported: int, bigint, uuid, varchar, text, email, date, datetime, boolean, decimal, json.

After you submit, the result panel shows a complete SQL script. You can copy it with one click, download it as a .sql file, or edit it to match your exact schema (constraints, defaults, triggers, or relationships).

Key Features

Dialect-aware output

Different databases use slightly different quoting rules and conventions. This generator can format identifiers and values in a way that fits common workflows for MySQL/MariaDB and PostgreSQL-style syntax, making it easy to paste into your preferred client.

Type-driven dummy values

Instead of producing random strings everywhere, the tool uses type hints to generate realistic examples: emails that look like emails, dates that look like dates, booleans as 0/1, numeric values within sensible ranges, and UUIDs for distributed identifiers.

SQL-safe escaping

Generated strings are quoted and escaped to avoid broken scripts. Apostrophes are handled properly, and JSON values are emitted as valid JSON text inside SQL string literals when needed.

Configurable row count

Choose how much data you need. Generate a handful of rows for a UI demo, or a larger batch for pagination testing, query planning checks, and realistic load simulations.

Script-friendly options

Enable an optional header that adds helpful comments and can wrap the inserts in a transaction-style section when appropriate. This makes the output easier to read, share, and rerun in a consistent way.

Use Cases

Frontend development – Populate a table so the UI can be styled with real-looking records, avatars, dates, and statuses.
API prototyping – Create sample entities quickly so endpoints return representative data without needing production dumps.
QA and bug reproduction – Share a minimal SQL snippet that reproduces edge cases across environments.
Performance checks – Generate enough rows to see how indexes, filters, and joins behave on realistic distributions.
Demo databases – Build a believable demo dataset for stakeholders without exposing real user information.
Education and tutorials – Produce clean examples for classes, blog posts, and documentation where you need consistent sample data.

Because the output is plain SQL, you can use it almost anywhere: database consoles, CI pipelines, local development containers, or quick “scratch” databases created for a short-lived experiment.

Optimization Tips

Match constraints early

If a column is unique, add a type hint that naturally generates distinct values (like uuid or email). For enum constraints, use enum(a,b,c) so every row remains valid.

Use realistic lengths

If your schema uses varchar(60) or varchar(120), include the length in your hint. That ensures generated text stays within bounds and helps catch UI truncation issues before production.

Scale gradually

Start small (10–50 rows) to validate your schema and inserts. Then scale up to hundreds or thousands using repeated runs when you want to explore query plans, caching behavior, and pagination performance.

FAQ

The tool targets common SQL syntax used by popular engines. You can pick a dialect preset (such as MySQL/MariaDB or PostgreSQL-style quoting) to format identifiers and values in a way that is easy to run in your environment.

Yes. Add one column per line in the format name:type. For large schemas, copy the column list from your DDL, adjust the type hints, and generate inserts in bulk. You can also generate in batches if you need a very large dataset.

Strings are generated with SQL-safe escaping in mind. If a value contains an apostrophe, it is escaped by doubling the quote character so the script remains valid when executed.

For repeatable scripts, keep your column hints stable and generate a fixed row count. If you need strict reproducibility across runs, you can also paste the generated SQL into version control and treat it as a deterministic seed file.

This tool generates dummy values intended for development and testing. It does not load or process your real database records. For privacy and compliance, avoid exporting real user data; use generated dummy data whenever possible.

Data Quality Notes

Realistic patterns without real people

The generator creates values that look familiar—like a person’s name, a company-like domain in an email, or a plausible timestamp—without relying on any private data source. That makes it ideal for screenshots, demos, and shared environments where privacy matters.

Predictable ranges

Numeric fields are filled using sensible ranges so you can test sorting, filtering, and validation rules. For example, integer IDs increment naturally, decimal fields use two decimal places, and boolean fields alternate often enough to exercise conditional UI states.

Friendly to constraints

When you use type hints like uuid, email, or enum(a,b,c), the tool aims to produce values that are compatible with typical constraints. This reduces the frustration of running a script only to discover a uniqueness or check constraint failure halfway through.

Practical Examples

Example: Customers table

For a simple CRM demo, you might use lines such as id:int, full_name:varchar(120), email:email, created_at:datetime, and is_active:boolean. Generate 25 rows and you instantly have enough data to test list views, search, and pagination.

Example: Orders table

To test totals, discounts, and status labels, define columns like order_id:uuid, customer_id:int, status:enum(pending,paid,shipped,cancelled), total:decimal, and ordered_at:datetime. The generated script gives you a quick baseline dataset that you can join to other tables as your schema grows.

Example: Audit logs

For admin dashboards, log tables often include action:varchar(60), actor_email:email, ip_address:varchar(45), and created_at:datetime. A realistic-looking set of audit entries helps you validate long-text wrapping, timestamp formatting, and filtering by action type.

Best Practices for Dummy Data

Separate “seed” data from “test” data

Seed data usually represents canonical records that your app depends on—like roles, permissions, currencies, or default settings. Test data is everything else and can be regenerated freely. Keep them separate so you can reset test data often without losing required configuration.

Use multiple distributions

Real datasets are rarely uniform. Consider running the generator more than once with different enum values or status columns to create skewed distributions. That helps you see how the UI behaves when one status dominates, and how queries perform when filters select only a small subset.

Validate with constraints and indexes

After inserting dummy rows, run your most common queries and check that indexes are being used. If you rely on partial indexes, composite keys, or unique constraints, dummy data that respects your schema makes it easier to spot slow queries early.

Why Choose This Tool

When you just need inserts quickly, simplicity beats complexity. This generator removes the overhead of setting up libraries, writing custom scripts, and juggling dependencies. It is fast, predictable, and produces human-readable SQL that you can tailor to your exact needs.

Whether you are building a demo, debugging a query, or preparing a training database, you can generate consistent, schema-friendly dummy data in a few clicks and stay focused on shipping features.