I keep my nodes short and sweet. They do one thing and do it well. I turn almost every DataFrame transformation into its own node. It makes it must easier to pull catalog entries, than firing up the pipeline, running it, and starting a debugger. For this reason many of my nodes can be built from inline lambdas.

Examples

Here are two examples, the first one lambda x: x is sometimes referred to as an identity function. This is super common to use in the early phases of a project. It lets you follow standard layering conventions, without skipping a layer, overthinking if you should have the layer or not, and leaves a good placholder to fill in later when you need it.

Many times I just want to get the data in as fast as possible, learn
about it, then go back and tidy it up.

from kedro.pipeline import node

my_first_node = node(
   func=lambda x: x,
   inputs='raw_cars',
   output='int_cars',
   tags=['int',]
   )

my_first_node = node(
   func=lambda cars: cars[['mpg', 'cyl', 'disp',]].query('disp>200'),
   inputs='raw_cars',
   output='int_cars',
   tags=['pri',]
   )

Note: try not to take the idea of a one liner too far. If your
one line function wraps several lines down it probably deserves to be
a real function for readability and a good docstring.