Awesome Apache Airflow

Overview

Apache Airflow is a platform to programmatically author, schedule, and monitor workflows. This list collects resources for learning and mastering Airflow.

Features

Core Capabilities

• Dynamic pipeline generation with Python code
• Extensible through custom operators and hooks
• Rich scheduling and execution semantics
• Web UI for pipeline monitoring and management
• Integration with major cloud platforms and databases
• Scalable distributed execution

Official Providers and Integrations

Cloud Platforms

• AWS (S3, EMR, Redshift, SageMaker, Athena)
• Google Cloud (BigQuery, GCS, Dataproc, Composer)
• Azure (Blob Storage, Data Factory, Databricks)

Databases

• PostgreSQL, MySQL, SQL Server
• MongoDB, Cassandra, Redis
• Snowflake, BigQuery, Redshift

Data Processing

• Apache Spark (via EMR, Dataproc, or standalone)
• Apache Kafka and Kafka Connect
• dbt (data build tool)
• Great Expectations (data quality)

Container Orchestration

• Kubernetes (KubernetesPodOperator)
• Docker (DockerOperator)
• ECS and Fargate

Popular Plugins and Extensions

• astronomer-cosmos: Run dbt projects as Airflow DAGs
• airflow-dbt: Integration with dbt
• airflow-exporter: Prometheus metrics exporter
• airflow-diagrams: Generate visual diagrams of DAGs

Managed Services

• Astronomer: Enterprise Airflow platform
• Google Cloud Composer: Fully managed Airflow
• AWS MWAA: Managed Workflows for Apache Airflow
• Qubole: Data lake platform with Airflow

Development Tools

• airflow-testing: Testing frameworks for DAGs
• airflow-maintenance-dags: Utility DAGs for cleanup
• airflow-code-editor: Edit DAGs in web UI
• astronomer-airflow-cli: CLI for local development

Best Practices

• DAG design patterns and anti-patterns
• Testing strategies for workflows
• Performance optimization techniques
• Deployment and CI/CD workflows
• Monitoring and alerting setup
• Security and access control

Learning Resources

• Official Apache Airflow documentation
• Airflow Summit talks and presentations
• Blog posts and tutorials
• Books: "Data Pipelines with Apache Airflow"
• Astronomer documentation and guides

Use Cases

• Batch ETL/ELT pipelines
• ML model training and deployment workflows
• Data warehouse maintenance and analytics
• Report generation and distribution
• API data ingestion
• Cross-system data synchronization
• Monitoring and alerting workflows

Pricing

Apache Airflow is free and open-source under the Apache 2.0 license. Managed services have subscription or usage-based pricing.