Hackolade Studio allows you to generate fake but realistic data for your data models. Using fake data can be useful during system development, testing, and demos, mainly because it avoids using real identities, full names, real credit card numbers or Social Security numbers, etc. while using “Lorem ipsum” strings and random numbers is not realistic enough to be meaningful. Alternatively, one could use cloned production data, except that it generally does not exist for new applications, you would still have to mask or substitute sensitive data to avoid disclosing any personally identifiable information.
“Synthetic data is also useful for exploring edge cases that lack real data or for identifying model bias.”
Penpal
function to data model attributes
All you need to do is to copy a function from the Faker guide pages and paste it in the property in Hackolade, for example:
You can check the result in the JSON Data pane of the JSON/YAML Preview tab for the entity. If a function is not correctly composed or is missing.
Assign a Faker function at the entity level
Assign a Faker
Since Hackolade Studio version 7.5.0, it is now possible to define a Faker function at the entity level, ensuring consistent and related attributes within an entity. This approach is beneficial for generating coherent attributes, such as creating an email address
Since Hackolade Studio version 7.5.0, it is now possible to define a Faker function at the entity level, ensuring consistent and related attributes within an entity. This approach is handy for generating coherent attributes, such as creating an email address from a person’s first and last name.
Since Hackolade Studio version 7.5.0, it is now possible to define a Faker function at the entity level, ensuring consistent and related attributes within an entity. This approach is handy for generating coherent attributes, such as creating an email address from a person’s first and last name.
