I have worked with Airflow during the past three years, but recently we adopted Dagster and I have been using it for the past 3 months. I have found it quite joyful to use and the experience has been very positive. Its main advantages compared to Airlfow (IMO):

  - A great UI
  - It forces you to clearly define inputs, outputs and types.
  - Separation of concerns: Between configuration and data, between processing and IO, and between code and deployment options.
  - It allows you to define flexible dags which you can configure at runtime, which makes it easiy to run locally or in k8s, or to switch the storage backend depending on the environment.

This blog post by the founder outlines the differences between the two in much more detail: https://dagster.io/blog/dagster-airflow