Airflow
Airflow is an open-source workflow management platform created by Airbnb. It allows users to programmatically author, schedule and monitor workflows. Airflow is useful for data pipelines, ETL processing, and machine learning workflows.
workflow
scheduling
etl
pipelines
airbnb
Airflow: Open-Source Workflow Management Platform
An open-source workflow management platform created by Airbnb, allowing users to programmatically author, schedule and monitor workflows for data pipelines, ETL processing and machine learning.
What is Airflow?
Airflow is an open-source workflow management platform created by Airbnb in 2015. It is written in Python and allows users to programmatically author, schedule and monitor workflows for data processing, data integration, and machine learning.
Some key features of Airflow include:
- A directed acyclic graph (DAG) visualization of job processes and dependencies
- The ability to schedule and monitor workflows through a web UI
- Extensibility through operators and hooks that allow integration with many services and platforms
- A modular architecture that separates the workflow definition from the underlying execution environment
- built-in alerting, logging, and failure handling
Airflow is commonly used by data engineers, data scientists, and devops teams. Typical use cases include daily/hourly data ETL jobs, automated machine learning pipelines, business metrics dashboards, and large-scale data processing on the cloud. Its popularity has increased due to its scalability and extensibility for managing large workflows across distributed systems.
Some alternatives to Airflow include Apache Oozie, Azkaban, Kubeflow Pipelines, Prefect and Dagster. However, Airflow remains a top choice for open-source workflow orchestration due to its active community support and ecosystem of operators, executors, and plugins.
Airflow Features
Features
- Directed Acyclic Graphs (DAGs) - allows users to author workflows as code
- Dynamic task scheduling - schedules tasks while respecting dependencies
- Extensible architecture - allows easy integration with custom plugins
- Scalable - horizontally scalable, parallelism at the task level
- Web UI - visualize pipelines and monitor task execution
- Command Line Interface - interface for managing and interacting with DAGs
Pricing
- Open Source
Pros
Open source and free
Large and active community support
Integration with wide range of technologies
Easy to get started for simple use cases
Web UI for monitoring and administration
Cons
Steep learning curve for complex workflows
No visual workflow designer
Limited support and documentation
Upgrading between versions can be challenging
Not optimized for real-time, low latency workflows
Official Links
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