Checking coding standards

Checking that code you write meet some standards is a very common thing in software development. Unfortunately it's not that easy to perform this task in Databricks notebook environment. And that's why pylint for Databricks was created.


  • Better readability - if everyone follows same standards it's easier to read code after someone else
  • Early problem detection - linting tools like pylint can discover issues like missing import or undefined variable pretty easily
  • Productivity - Using linting tools leads to better productivity, maybe it doesn't seem that way at first but in long run after people get used to it, it really does


If you have existing project all you need to do is

In new daipe projects everything above is already setup

How to use it

There are two main ways to use pylint

User flow

As a user you write a normal notebook code.

If you want to check your code with pylint, you just go to the tools/pylint notebook and click Run All. And that's it.

When the notebook finishes you should see pylint results at the bottom of notebook.

You can click on the problematic notebooks/files, and you will be automatically redirected to the exact cell with the problem.

You can see that pylint tells me that it can't import FeatureStore from featurestorebundle because it is not installed. So it will also detect missing dependencies in your pyproject.toml.

If you forget where the problem was you don't have to go back to pylint notebook. We included hint in the redirect URL where you can see the cell and line number where the problem is.

CI/CD flow

As always people are not as reliable as computers in performing automated tasks. And that's why you should also enable additional linting check in your CI/CD pipeline.

If you are using our centralized Github CI/CD pipelines it is pretty easy to enable linting check. Just enable pylint in your build pipeline like that.

    uses: daipe-ai/daipe-project-ci-cd/.github/workflows/build.yml@v1
      pylint_enabled: true