Fabric Airflow and dbt thrive to help you deliver reliable data using common interface (Python, SQL and CLI) in collaborative and ecosystem friendly manner. Airflow helps orchestrate jobs that extract data, load it into a warehouse, and handle AI/machine-learning processes. dbt enables SQL transformation of the data that has already landed in the warehouse.

Let’s learn how Fabric Airflow can be used to orchestrate dbt jobs, meaning you can use Airflow to schedule and manage dbt projects, ensuring that your data transformation models are executed in the correct order and with the appropriate dependencies. Not only that, but you also get your data quality checks performed as part of DAG (direct acyclic graph) code.

In this session, we will cover Fabric Airflow and dbt architecture. We will differentiate on how it differs from open source and other products. With Fabric focus, Airflow DAG operators and dbt models accelerate your ETL, ELT tasks along with data quality completion at speed with super easiness of vivid SaaS experience.