Airflow and dbt are complementary tools that enhance collaboration across data teams by addressing data orchestration and transformation, respectively, forming an integral part of modern data stacks. They can be seamlessly integrated, with options depending on whether teams are using dbt Core or dbt Cloud. Airflow's flexibility allows for custom data ingestion workflows, while dbt Core is used for data modeling, supported by methods like the dbt CLI with BashOperator or KubernetesPodOperator. The recent introduction of dbt 1.0 simplifies rerunning jobs from failure, enhancing workflow efficiency. Meanwhile, the new dbt Cloud Provider, developed by Astronomer and dbt Labs, offers an operator, hook, and sensor to orchestrate and monitor dbt Cloud jobs using Airflow, eliminating the overhead of dbt Core and providing a unified interface for data engineers and analysts. This integration allows data teams to focus on designing data pipelines and models without being burdened by infrastructure management.