casual VS faker

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:

Instead of manually having to think of defaults for your interface properties, you could use Faker.

If you think you have something which other Faker users would find useful, you can contribute it to Faker! See the contributing guide, you can create an issue or a pull request.

First, we create a react project, and then we use a service faker to create 25000 user records.

Use faker.js to generate random values for your mock data.

I built a starter project for developing a social media app with Sveltekit, Lucia Auth, Prisma and Faker.js. Uses sqlite for prototyping but can easily be changed to Postgres or MySQL.

Don’t even need chatGPT or json files! There are libraries like faker that’ll do it on the fly for you!

I am currently working with Typescript as well as C++ and I enjoyed using FakerJS library for testing data. I've checked for C++ libraries and I haven't found anything that could be used for my basic needs (like generating emails, passwords, names, uuids or lorem words) so I just started my own project with idea to deliver such library to C++ developers.