Data Generator

How to generate test data

Follow these steps to configure and generate your test data:

1. Set the number of fields: Start by adding the desired number of fields for your dataset.
2. Name each field: Assign a unique name to each field to easily identify it.
3. Choose the data type for each field: Select the data type (e.g., string, integer, date) for each field.
4. Configure field options: Provide any additional options for each data type (e.g., minimum/maximum values for numbers).
5. Customize field arrangement: You can remove or reorder fields at any time to match your desired structure.
6. Set the number of rows : Define how many rows you want to generate.
7. Configure format: Select the format (CSV, SQL, XML and JSON). For CSV format, customize the output by selecting the delimiter, quote, escape character, newline format, and whether to include a header row...
8. Generate data: Click on "Generate" button. The data file will be automatically downloaded.

Saving your template

You can save templates to reuse your configurations for future data generation:

• Save your template: Click "Save current template" to store your current template locally in your browser. You can specify the template name.
• Copy the current template: Use the "Copy current template" button to duplicate a template and modify it as needed.
• Manage your templates: In "Your templates," you can view by clicking on the template name, and delete saved templates by clicking the delete button.
• Backup templates: Your templates are saved in your browser. You can export them to a file by clicking the "Export current template to file" button, allowing you to import them on another computer or browser by clicking the "Import template from file" button.

Test Data Generation

Test data is valuable in various projects, such as web applications, APIs, or data science solutions. Having reliable and realistic datasets allows for effective testing of functionality and performance without using real data.

When to Generate Test Data?

Test data is essential in many cases: during the development of applications that require databases, the creation of data analysis pipelines, or even in building mockups to demonstrate how a solution works. It is also useful for testing different scenarios, such as search queries, transactions, or data migration processes. Generating synthetic data also helps protect sensitive data in simulations.

SQLable Data Generator

SQLable's data generator quickly and easily creates test datasets. Whether you need data in CSV, SQL, JSON, or XML formats, this dataset generator allows to precisely define data types, generation rules, and desired volumes. You only need to specify the columns, field types, and any specific constraints, and the tool will generate data ready to be injected into databases or used in your development environment. Data generation is quick, even for large volumes, which is difficult to achieve with current GPT-based tools.

I hope this tool will be useful to many!