Productivity features prevent your pipeline list from turning into a catalogue that is difficult to browse.
Use favorites
Reserve favorites for workflows you genuinely run often. A short list remains more useful than a second exhaustive list.
Create templates
A template is ideal when the same structure serves several contexts: import, standard cleanup, and export, with a few settings that vary.
Keep one responsibility per pipeline
A pipeline should solve an identifiable goal. Several short recipes are often easier to maintain than one very long workflow.
Test with a small sample
Before processing a large file, validate the result on a few rows. The preview and local suggestions can help you spot empty columns or duplicates.
Time saver: state the purpose in the name and use a consistent convention across your pipelines.
A simple convention
Use a stable structure such as “data type — action — destination,” and add a date only when it genuinely identifies a version. Review favorites and templates periodically: a shorter catalogue reduces selection mistakes and makes important recipes immediately visible.
Organize a real catalogue
Adopt a convention such as “source — action — output”: “CRM — deduplicate contacts — CSV.” A template describes a structure to instantiate; a pipeline is a directly executable recipe. Do not turn every successful pipeline into a template.
Every month, remove occasional recipes from favorites, archive experiments, and review shared templates. Keep a small example file for important pipelines so that you can detect a regression after a change.
Common anti-patterns include names such as “final test 2,” chains that solve several unrelated goals, and copies that cannot be distinguished from one another. A short list, focused responsibilities, and clearly named variants provide more productivity than an exhaustive catalogue.