The blog post discusses the development of a Python package named Cosmos, designed to facilitate running dbt models on Airflow by generating Airflow DAGs from dbt models more conveniently. This piece serves as the third installment in a series exploring the integration of dbt transformation pipelines within an Airflow DAG, utilizing the dbt manifest.json file to map each model to a task. The post introduces a utility, DbtDagParser, which simplifies the creation of Airflow task groups by parsing dbt models and dependencies, offering flexibility through features like the "dbt_tag" parameter for selective model execution. While the method provides fine-grained control over model execution, it may impact performance due to the overhead of calling "dbt run" for each model. Tests showed that a mapped DAG took longer to execute than a single-task DAG, highlighting tradeoffs concerning runtime and control. The post encourages users to evaluate this pattern against their needs, keeping in mind the performance implications based on their dbt model structures and data volumes, and hints at further explorations of dbt and Airflow integration in future posts.