Testing is a fundamental software practice. But people don't do it in data because it's hard. We're making it less hard (and more fun).

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The post discusses the challenges and solutions for testing in data engineering. It highlights several key obstacles, such as data variability, complex transformations, and lack of tooling. Tinybird aims to address these issues with tools like 'tb mock' for generating realistic test data, and 'tb test' for validating data transformations. The use of LLMs to handle mundane aspects of test generation is emphasized, making testing less tedious and more efficient.

Writing tests sucks. Use LLMs so it sucks less.

Generating realistic test data with tb mock

Write tests yourself. It doesnt suck. It helps you understand the code you have written.

Backend devs are all about writing tests—no debate there. Frontend devs? Not so much. Usually, it’s the perceived hassle or overhead that keeps them away. But once you dive into testing the frontend, your dev flow hits another level: 10xx* faster, way less errors, and a whole lot cleaner. Testing isn’t just a chore—it’s a cheat code.

<a href="https://app.daily.dev/daniel8000" data-mention-id="SOwG1kJqf6HK6TkGmZH45" data-mention-username="daniel8000" translate="no">@daniel8000</a> , I agree with you on it helps you understand the code but since I have been writing tests since it wasn’t fashionable, I ask AI to do most of the heavy lifting and modify the AI’s code to fit my needs.