Deploy Bruin with Apache Airflow
Orchestrate Bruin pipelines from Apache Airflow using BashOperator or KubernetesPodOperator, with secure credentials and scheduled DAGs.
What
Goal - Run Bruin pipelines from an existing Apache Airflow deployment.
Best for - Teams that already operate Airflow and want Bruin CLI inside their existing DAG scheduling and monitoring layer.
Prerequisites
- Apache Airflow installed and running
- Access to deploy DAGs and configure workers or Kubernetes pods
- A Bruin project ready to deploy
- Production data platform credentials
- Worker or pod network access to your data platforms
Bruin CLI runs cleanly as a task inside Airflow. If you do not want to operate Airflow, Bruin Cloud provides managed scheduling, monitoring, lineage, runs, backfills, notifications, and secure connection management.
More guides
Chat with an AI Agent
Use Bruin Cloud's chat to ask an AI agent about your data, generate reports, and run Bruin Cloud CLI tasks like pipeline status and history.
Configure AI Agents
Create and configure AI agents in Bruin Cloud - pick a project, add messaging integrations, attach a connection set, and set permissions.
Connect Bruin Cloud MCP to Claude Code
Set up the Bruin Cloud MCP so your AI agent can query pipelines, inspect runs, and trigger actions in Bruin Cloud directly from your terminal.