3. Methods of synthetic data generation
Explore 3 of the most commonly used methods of data generation within the Curiosity Platform, and learn when and why to choose them.
When using the Curiosity Platform, there are multiple methods available to populate the rule sets needed to generate data. There are also different methods of activating synthetic data generation, and users can use a combination of these methods to configure the rule needed for each of their use cases.
It’s also possible to execute the synthetic data generation from multiple routes. These include: using an API call such as from an automation framework or CI/CD pipeline, using a submit form from the self-service portal, or using an iFrame to integrate into another platform such as a Confluence.
Using defaults, data painter and rule set accelerators
There are several ways to configure rule sets in the Curiosity Platform, and the three most common are covered in this training. You will learn how to use ‘Defaults’, ‘Data painter’ and ’Rule set accelerators’.
Defaults are great for standard synthetic data generation rules that are commonly seen within organisations and can be used to solve common use cases.
Data Painter allows the user to create or modify functions typically for advanced or rare use cases.
Rule set accelerators can be used to quickly configure a rule set to accomplish specific tasks, such as to clone a specific set of data and create copies from it.