Mock Data Generation

Overview

Mock data generation is the process of creating realistic synthetic datasets to populate databases with representative data for testing, prototyping, and demonstration purposes..

Neurelo offers users this capability to rapidly populate your databases with mock data. We utilize AI to intelligently generate mock data that aligns with your schema and its attribute contexts. All our supported data sources (PostgresSQL, MySQL, MongoDB) are compatible with this feature.

This functionality is especially valuable in testing and development scenarios, allowing for the validation of new features, bug fixes, and performance optimizations within a controlled environment.

How to generate mock data?

To generate mock data, you must have an existing project with a defined schema and an environment that connects to your schema and its corresponding data source.

• Select your project from Neurelo's dashboard

• Inside the project, select Environments from the left navigation panel. This should open the page that list all your Environments inside this project.

From this page select "Data Generator" for a specific environment in the list.

or, you can select and go into the details for a specific environment and you will find the "Data Generator" button in the card at the top for that environment.

Click on the button to launch our Mock Data Generator dialog. You will then be prompted to select the size of the dataset you wish to generate.

Currently, we offer three sizes: small (with approx. 10 rows), medium (with approx. 100 rows), and large (with approx. 1000 rows). Essentially, this means that each table/collection will be populated with the number of rows you select. Choose the desired size and click on the "Start" button. A notification stating “Mock data generation in progress” will appear in the top right corner of the environments page.

Please wait until the mock data generation is complete. Note that populating 1000 rows may take longer, especially with complex schemas. Upon successful mock data generation, a “Mock data generation completed” message will be displayed.

Restrictions and Limitations

Please note the following requirements for the Mock Data Generation service:

• The database must not contain any existing data in any of the tables. This constraint ensures that users do not inadvertently overwrite their existing data. We strongly advise only utilizing this feature within a test environment that incorporates a test database. For those looking to conveniently erase their existing test databases, we offer a "Wipe Data Source" option under the Data Sources tab. By default, this feature is disabled and requires the disabling of wipe protection. Please refer to Wipe Data Source for more details.

• If there are pending migrations, they need to be applied before performing mock data generation. This ensures that the schema for which you are generating your mock data is consistent with the schema in your database.

Furthermore, our Mock Data Generation service has the following limitations:

• If your schema contains warnings, we cannot guarantee the successful generation of mock data. In such cases, you might encounter partially mocked data. We advise resolving any warnings prior to proceeding with the mock data generation for your database.

• Cyclical references within the schema's relationships are not supported.

• Schemas that include composite types are not supported.

• The generation of mock data for multiple schemas with PostgreSQL is currently not supported.