The opposite of configuration-is-code
"Configuration as code" is a principle we stand by for this purpose. While yaml or json job configuration would allow for any language to be used to generate Airflow pipelines, we felt that some fluidity gets lost in the translation. Being able to introspect code (ipython!, IDEs) subclass, meta-program and use import libraries to help write pipelines adds tremendous value. Note that it is still possible to author jobs in any language or markup, as long as you write Python that interprets these configurations.
We believe the most effective approach is to accept this need, embrace the inevitability of programmatic configuration, and maintain a clean separation between computation and data. The language to represent the data should be a simple, data-only format such as JSON or YAML, and programmatic modification of this data should be done in a real programming language, where there are well-understood semantics, as well as good tooling. Interestingly, this same separation of computation and data can be seen in the disparate field of front-end development with frameworks such as Angular that maintain a crisp separation between the worlds of markup (data) and JavaScript (computation).