Tutorial: Apache Airflow job editor¶
CSA Loom
airflow-jobeditor — DAG-based orchestration on managed Apache Airflow (preview) for workloads that need Airflow operators beyond what ADF / Synapse pipelines cover. No Microsoft Fabric required.
What it is¶
An Apache Airflow job runs DAGs synced from a Git repo on a managed Airflow environment. Use it when you need Airflow-native operators (Spark, dbt, Snowflake, HTTP, sensors) or an existing Airflow investment that ADF / Synapse pipelines don't cover.
When to use it¶
- Your team already authors Airflow DAGs and wants to keep that workflow.
- You need operators or scheduling semantics (sensors, backfills, dynamic DAGs) that visual pipelines don't expose.
- You orchestrate tools like dbt or Snowflake alongside Azure services from one scheduler.
Step-by-step in Loom¶
- Create the item. Choose + New item → Apache Airflow job (Data Factory). The editor opens at
/items/airflow-job/<id>. - Connect a Git repo. Point the managed Airflow environment at the Git repo that holds your DAG definitions.
- Sync DAGs. DAGs sync from the repo and appear in the Airflow environment for scheduling.
- Use Airflow operators. Author tasks with native operators (Spark, dbt, Snowflake, HTTP) that ADF / Synapse pipelines don't expose.
- Mind the preview gate. This is a preview item; if the managed Airflow runtime isn't provisioned the editor surfaces the exact env / bicep requirement as an honest MessageBar — nothing is faked.
The Azure backend it rides on¶
- Runtime: managed Apache Airflow (Workflow Orchestration Manager) in Azure Data Factory.
- Source of truth: your Git repo — DAG authoring stays in code.
No Fabric required¶
The Airflow environment is an Azure Data Factory capability; no Fabric capacity, workspace, or OneLake is involved on the default path.
Learn more¶
- Workflow Orchestration Manager (managed Airflow): https://learn.microsoft.com/azure/data-factory/airflow-overview