casual VS datafaker

Faker looks cool! We've been using Casual [0] for ages but Faker looks more featureful and more actively maintained.

0: https://github.com/boo1ean/casual

In order to create the mockserver we need 2 npm dependencies. json-server and casual so we run npm install json-server casual --save-dev in our project.

https://npm.im/casual (57k weekly downloads) https://github.com/boo1ean/casual (2.8k stars)

In the past, I have relied on Google Sheets and a combination of array formulas, vlookups, random value generators to create fake datasets. This approach worked for small, one off datasets. Any time I needed to generate more than 1000 records or needed to update a dataset, this approach proved to be tedious. I also explored Faker and Casual (both of which are fantastic tools) but found that the data they generate was too abstract and that the implementation wasn't ideal for large datasets. I wanted a tool that:

Looks like that link doesn't actually go where it says, try this https://github.com/datafaker-net/datafaker

Give it a try https://github.com/datafaker-net/datafaker

Looks a lot like DataFaker Any pros and cons or differences compared to this?

Great library, and thanks for sharing. I maintain something add similar but different (https://www.datafaker.net), and it would be interesting to be able to use these libraries together for example.

For example, I run a project (https://www.datafaker.net), which is currently a Java application, but we'd like to extend it with a Kotlin module.

I'm running a project myself, https://www.datafaker.net, a project to generate real looking test data.