Tool Overview

The Test Data Generator creates structured datasets based on a schema you define. You choose fields, give them names, assign types such as UUID, full name, date, price, or boolean, and then specify how many rows you want. The tool generates rows that respect this structure and lets you preview the result, copy it, or download it in JSON or CSV formats.

Many applications and databases need rich test data during development and QA. Hand-building this data is slow and often too small to reveal real world issues. Using production data can be dangerous for privacy and compliance. This generator gives you a safe and repeatable way to create realistic records that match your domain without ever touching live user information.

The tool is designed for developers, QA engineers, database administrators, data engineers, and students. Beginners can rely on sample schemas to get started. Technical users can refine fields and constraints to match complex models. Professionals can export large datasets for seeding databases, populating local development environments, or testing API behavior at scale.

Background & Concept Explanation

Test data is any data you use to validate that your software behaves as expected. Good test data covers typical cases and edge cases, and resembles real production data in shape and variety. For relational databases or JSON APIs, this means having properly typed fields such as identifiers, names, dates, prices, percentages, and boolean flags. When tests run only on small or artificial datasets, bugs in parsing, validation, and performance often remain hidden.

Traditionally, teams built test data by writing SQL INSERT scripts, maintaining static JSON files, or extracting anonymized subsets of production data. Each approach has drawbacks. Manual scripts quickly go out of date when the schema changes. Static files may not match new fields or constraints. Production-derived data raises questions about consent and retention. An ideal approach is to generate fresh, synthetic data from a schema whenever you need it.

The Test Data Generator follows this schema-first idea. A schema is a list of fields, where each field has a name, a type, and optional options such as minimum and maximum values or prefixes and suffixes. The tool treats this schema as a contract: every generated row will contain all the fields and each value will conform to the specified type. Because the generator understands types, it can create realistic values without your having to write custom code for each field.

Key Features

• Schema-based configuration. You define your dataset by composing a list of fields. Each field has an id, a human-readable name, a field type, and optional options like numeric ranges or string prefixes. This schema forms the blueprint for all generated rows.
• Rich set of field types. The tool supports many common test data types, including UUID, auto increment numbers, full name, first name, last name, email address, phone number, recent dates, past dates, prices in currency, percentages, booleans, countries, cities, random words, and lorem ipsum text. Each type has tailored generation logic.
• Sample schemas for common scenarios. A row of sample schema buttons shows templates like User Profile, E-commerce Order, Product Catalog, and API Response. Each sample provides a realistic set of fields for that domain, such as orderId, customerName, orderDate, and totalAmount for orders. Clicking one instantly populates the schema editor.
• AI-assisted schema drafting. An AI drafting modal lets you describe your desired dataset in natural language, for example “a list of support tickets with id, subject, status, and created date.” The tool sends this prompt to a backend AI service and maps the returned field definitions into the schema editor. You can then refine field names and types as needed.
• Schema import via JSON. An upload button accepts JSON files containing arrays of field definitions. The tool parses the file, confirms it is a valid array, and maps each entry into a SchemaField. This is useful when you store schemas in version control and want to reuse them.
• Automatic live preview. Whenever the schema changes, the tool regenerates a small preview dataset (for example five rows) and renders it in a code-like panel. You can toggle between JSON and CSV views. This immediate feedback helps you confirm that fields and types behave as expected before generating full datasets.
• Row count control with limits. A numeric input and helper text let you choose how many rows to generate for download. The tool enforces a maximum row count to prevent performance issues. If you type a number above the limit, it clamps the value and displays a note about the maximum.
• Format selection for export and preview. A simple toggle lets you choose JSON or CSV format. The preview panel and export logic both respect this choice, so what you see in the live preview reflects what will be downloaded or copied.
• Error reporting and validation. The UI surfaces clear errors when you exceed limits (fields, rows, prompt size), upload invalid JSON, or attempt actions without a valid schema. Errors appear in a compact banner that you can dismiss, with messages that explain how to correct the issue.
• Copy to clipboard functionality. A Copy button in the preview header copies the current preview content (JSON or CSV) to your clipboard. If the copy succeeds, the button briefly shows “Copied!” so you know the text is ready to paste into your editor or tooling.
• Download full dataset. A Download Dataset button assembles a dataset with the requested row count and downloads it as a file with a timestamped name. JSON exports use application/json content type, and CSV exports use text/csv, making them easy to open in code editors or spreadsheet tools.
• Responsive interface. The layout adapts between desktop and smaller screens. On large screens, you see the schema editor and preview side by side. On small screens, focus stays on one area at a time, with controls kept accessible and scrollable.

Common Use Cases

A backend developer writing new API endpoints needs realistic payloads to test validation and business logic. They choose the API Response sample schema, add a few extra fields, and generate thousands of rows in JSON format. They then feed these rows into automated tests to ensure the API behaves correctly.

A QA engineer testing a reporting feature needs synthetic orders and customers. They select the E-commerce Order schema, adjust field names to match their database, and generate several thousand CSV rows. They import this CSV into a test database and run UI tests to verify filters, aggregations, and edge cases.

A database administrator evaluating index strategies wants a product catalog with realistic prices, descriptions, and boolean flags. They load the Product Catalog sample, tweak min and max values for price using field options, and export a large dataset. They then analyze execution plans and performance on this synthetic load.

A teacher demonstrating data modeling in a classroom uses the User Profile sample schema to generate datasets that students can load into their own databases. Students practice writing queries against these tables without worrying about personally identifiable information.

How to Use This Tool (Step-by-Step)

1. Open the Test Data Generator. On the left, you will see schema configuration controls, and on the right, a live preview area.
2. Start by loading a sample schema if it matches your use case. Click one of the sample buttons such as User Profile, E-commerce Order, Product Catalog, or API Response. The field list below will populate with appropriate field names and types.
3. To build a schema from scratch, click the Add New Field button. A new field card appears with a default name and type. Enter a meaningful name such as userId or orderDate and pick a type from the dropdown list that best matches the data this field should hold.
4. Repeat the process for each field you need. Use the remove icon on a field to delete it if you add one by mistake or no longer need it. The tool prevents you from dropping below one field if you already have fields defined.
5. If you have an existing schema definition in JSON format, click the Upload button and select the file. The tool parses and validates the file. If it contains an array of field objects, it maps names, types, and options into the schema editor.
6. For AI assistance, click the Draft button in the toolbar to open the schema drafting modal. Describe your desired dataset in plain language, then click Generate. When the backend returns suggestions, they replace the current fields in the editor. Review and adjust these fields as needed.
7. Watch the live preview panel on the right. As you add or change fields, it regenerates a small set of test rows. Switch between JSON and CSV format using the format toggle so you can see how the dataset will look in each form.
8. Use the Rows to Generate controls to set how many rows you want in your final dataset. Type a number in the field or use the arrows to adjust. The tool enforces a maximum row limit, and the label below the input shows this maximum in human friendly form.
9. When you are ready to export, click the Download Dataset button. The tool generates a dataset with your requested number of rows and triggers a file download in the currently selected format. Save this file to your project, database seeding scripts, or test harness.
10. If you want to copy only the preview data instead of a full export, click the Copy button in the preview header. The data is copied in the format you selected (JSON or CSV). Paste it into your code, tool, or documentation as needed.
11. Any time you change the schema or row count, repeat the preview and export steps as needed. The generator always uses the latest schema and settings, so you can iterate on your test data design as your application evolves.

Calculations & Logic

The Test Data Generator calculates data values by combining random selection with simple rules that respect field types. For each field, it uses a type-specific generator. UUID fields use crypto.randomUUID to produce version 4 identifiers. Auto increment fields start from a minimum value (defaulting to 1) and increment by one per row, using the row index in the loop.

Full name, first name, and last name fields pick values from curated lists of common names, combining them as necessary. Email fields assemble a username from a random first and last name plus a random number, then append a domain from a list of popular email providers. Phone fields build a standard-looking pattern with a country code and segments of random digits.

Date fields come in two flavors. Recent dates choose random offsets within a recent time window such as the last 30 days, while past dates choose random offsets within a larger span like the last ten years. Prices use min and max options when provided and default values otherwise. The generator selects a floating point number within that interval, then rounds and parses it to keep decimal precision.

Percentage fields select integer values between 0 and 99. Boolean fields use simple thresholding on a random number to pick true or false. Country and city fields choose from arrays of plausible values. Random words fields join several random entries from a list of buzzwords, and lorem ipsum fields return a fixed block of placeholder text.

When generating the full dataset, the tool clamps the requested row count to a safe maximum to avoid performance problems. It then loops from 0 to safeCount - 1, constructing each row as a plain object. For each field, it chooses a key based on the field name (or a fallback using the field id) and sets the value from the type-specific generator. The final array is a list of these row objects.

For CSV conversion, the tool takes the keys from the first row as headers. It builds a header line by joining these keys with commas. For each row, it maps each header to a string value. If a value is a string containing a comma or quote, it wraps the value in quotes and escapes internal quotes. For other types, it uses JSON.stringify so that booleans, numbers, and nulls serialize clearly.

The AI drafting logic calls a backend tool identifier with your prompt string. The backend returns an array of simple field objects with name and type labels. The tool then maps each returned type label to one of the defined FieldType values, defaulting to random words if no match is found. It assigns unique ids to each suggested field and enforces the maximum number of fields.

Tips, Limitations & Best Practices

Keep in mind that generated data is only as good as the schema you design. Spend time naming fields clearly and choosing types that match how your application uses them. This makes it easier to plug test data into components or APIs without extra transformation.

Respect the configured limits for fields, rows, and prompt size. These safeguards exist to protect your browser from running out of memory or becoming unresponsive. If you need very large datasets, consider generating multiple chunks and loading them into your database in batches.

Use auto increment fields when you need stable, predictable identifiers, especially for relational tests. Use UUIDs when your real system uses them and you want to mimic that environment. Mixing both in a schema can help you test migrations between id strategies.

When using AI to draft schemas, treat suggestions as drafts, not as final designs. Review and adjust each field before generating data. Remove any unnecessary fields so your datasets remain focused and manageable.

Always avoid using generated datasets in production or for real users. They are synthetic and may not cover every edge case in live environments. However, they are excellent for local development, automated tests, and demonstration environments where privacy and repeatability matter.

Finally, store useful schemas and sample datasets in your version control system. This makes your test data strategy reproducible across environments and easier for teammates to adopt and improve over time.