Apache Airflow is a powerful open-source platform designed to author, schedule, and monitor workflows. It has become a popular choice for orchestrating ETL pipelines and machine learning workflows due to its extensible framework and robust tracking capabilities. In this guide, we will explore how to deploy Apache Airflow on Azure, leveraging its managed services for optimal performance and scalability.
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Understanding Apache Airflow Architecture
Airflow's architecture consists of several key components:
Web Server: Provides a user interface for monitoring and managing workflows.
Metadata Store: A relational database (MySQL/PostgreSQL) that stores metadata.
Persistent Volume: Stores DAG files.
Scheduler: Manages task execution.
Worker Process: Executes tasks.
Airflow supports various execution modes:
Sequential Executor: Ideal for development and testing, allowing one task at a time.
Local Executor: Supports parallel execution for small to medium workloads.
Celery Executor: Suitable for production, enabling scaling with RabbitMQ or Redis.
Dask Executor: Allows distributed task execution using Dask.distributed.
Deploying Airflow on Azure
Azure offers multiple options for deploying Airflow, including Azure VMs ,ADF and managed services. For production, managed services are recommended for high availability and scalability.
Using Puckel’s Airflow Docker Image
Puckel’s Docker image provides the latest Airflow build, integrated with Azure App Service for Linux. This setup supports continuous deployment and multi-container deployments with Docker Compose and Kubernetes, ideal for Celery Executor mode.
QuickStart Template for Azure Deployment
Azure's QuickStart template simplifies Airflow deployment using Azure App Service and Azure Database for PostgreSQL. It automatically downloads the Docker image and initializes the database, setting environment variables for configuration.
Key environment variables include:
AIRFLOW__CORE__SQL_ALCHEMY_CONN: Connection string for PostgreSQL.
AIRFLOW__CORE__LOAD_EXAMPLES: Loads DAG examples during deployment.
After deployment, access the web server UI on port 8080 to monitor workflows.
Advanced Deployment Options
For enterprise-grade deployments, consider Bitnami’s multi-tier Airflow template. Alternatively, deploy kube-airflow with Celery Executor on Azure Kubernetes Services using Helm charts, Azure Database for PostgreSQL, and RabbitMQ.
Conclusion
Deploying Apache Airflow on Azure provides a scalable and efficient solution for managing complex workflows. Whether you're testing or running production workloads, Azure's managed services and templates offer a streamlined deployment process. Explore these options to enhance your data pipeline orchestration and workflow management capabilities.
For more detailed instructions and advanced deployment strategies, visit our latest blog post on Bitnami’s Airflow solutions.