Astronomer
Managed Apache Airflow service for orchestrating and monitoring data pipelines in the cloud
Apache Airflow
Programmatically author, schedule, and monitor workflows as code
Apache Airflow is a platform for orchestrating complex workflows through code-defined DAGs, featuring a rich UI, extensible operators, and robust scheduling for batch data pipelines.
Overview
Workflow Orchestration as Code
Apache Airflow is a platform designed for teams that need to programmatically author, schedule, and monitor complex workflows. By defining workflows as code (DAGs), Airflow makes pipelines maintainable, versionable, testable, and collaborative—eliminating the brittleness of GUI-based workflow tools.
Built for Batch Processing
Airflow excels at orchestrating mostly static, slowly changing workflows where tasks are idempotent and delegate heavy computation to external systems. The scheduler executes tasks across worker arrays while respecting dependencies, and the rich web UI provides real-time visibility into pipeline execution, progress monitoring, and troubleshooting. While not a streaming platform, Airflow is commonly used to process real-time data by pulling from streams in batches.
Extensible and Production-Ready
With dynamic pipeline generation through Python code, Jinja templating for customization, and a wide library of built-in operators, Airflow adapts to diverse orchestration needs. Tested against PostgreSQL, MySQL, Kubernetes, and multiple Python versions, it supports both AMD64 and ARM64 platforms. The project is maintained by the Apache Software Foundation and widely adopted across industries for data engineering, ETL, and ML pipeline orchestration.
Highlights
Code-based DAG authoring with dynamic generation and parameterization
Rich web UI for visualizing pipelines, monitoring progress, and troubleshooting
Extensible operator library with Jinja templating for customization
Production-tested scheduler with dependency management across worker arrays
Pros
- Workflows as code enable version control, testing, and collaboration
- Extensive operator ecosystem and active Apache community support
- Flexible deployment options including Kubernetes and multiple databases
- Powerful UI for monitoring and managing production pipelines
Considerations
- Not designed for streaming or high-volume data transfer between tasks
- Installation complexity due to open dependency management
- Best suited for static workflows; frequent DAG structure changes reduce clarity
- POSIX-only production support; Windows requires WSL2 or containers
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Fit guide
Great for
- Batch data processing pipelines with idempotent tasks
- Teams requiring version-controlled, testable workflow definitions
- Orchestrating tasks across heterogeneous systems and external services
- Organizations needing production-grade scheduling with dependency management
Not ideal when
- Real-time streaming data processing requiring sub-second latency
- Workflows that pass large data volumes directly between tasks
- Highly dynamic pipelines with frequently changing DAG structures
- Windows production environments without containerization
How teams use it
ETL Pipeline Orchestration
Coordinate extraction, transformation, and loading across databases, data warehouses, and cloud storage with dependency-aware scheduling and retry logic.
Machine Learning Workflow Automation
Orchestrate model training, validation, and deployment pipelines with parameterized DAGs that integrate with MLOps tools and compute clusters.
Batch Processing from Streaming Sources
Pull data from Kafka or other streams in scheduled batches, process through idempotent tasks, and load into analytics platforms.
Multi-System Data Integration
Coordinate data movement and transformations across APIs, databases, and SaaS platforms using extensible operators and custom hooks.
Tech snapshot
Python92%
TypeScript6%
JavaScript1%
Shell1%
Go1%
Dockerfile1%
Frequently asked questions
Is Airflow suitable for real-time streaming?
No, Airflow is not a streaming solution. However, it is commonly used to process real-time data by pulling from streams in batches on a schedule.
What databases does Airflow support?
Airflow supports PostgreSQL (13-17), MySQL (8.0, 8.4, Innovation), and SQLite (3.15.0+). SQLite is only for development; PostgreSQL or MySQL are recommended for production.
Can I run Airflow on Windows?
For production, only POSIX-compliant systems (Linux) are supported. On Windows, use WSL2 or Linux containers for development and testing.
Why is pip install apache-airflow sometimes problematic?
Airflow keeps dependencies open for flexibility, which can cause conflicts. Use the provided constraint files from the constraints-main or constraints branches for repeatable installations.
Should tasks pass data between each other in Airflow?
Tasks should not pass large data volumes directly. Use XCom for metadata only, and delegate data-intensive operations to external systems like data warehouses or processing frameworks.
Project at a glance
Active- Stars
- 43,922
- Watchers
- 43,922
- Forks
- 16,323
LicenseApache-2.0
Repo age10 years old
Last commit13 hours ago
Primary languagePython
Last synced 12 hours ago