Apache Airflow

Apache Airflow is an open-source platform designed to programmatically author, schedule, and monitor workflows. With its Python-based coding environment, users can dynamically create pipelines that suit various data processing tasks. Airflow's web UI offers robust monitoring capabilities, while its modular architecture ensures scalability and extensibility.

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Platform created by the community to programmatically author, schedule and monitor workflows.

Apache Airflow

Key Features

• Programmatic Workflow Creation: Define workflows using pure Python code for maximum flexibility and control over your pipeline's logic.
• Scalable Architecture: A modular setup with a message queue enables Airflow to scale based on the workload demands effortlessly.
• Dynamic Pipeline Generation: The dynamic configuration allows for on-the-fly adjustments to your workflows as needed.
• User-Friendly Interface: A modern web application lets you schedule, monitor, and manage workflows with complete visibility into task statuses and logs.
• Extensive Integrations: Comes with a variety of pre-built operators that integrate seamlessly with popular cloud services like GCP, AWS, and Azure.
• Ease of Use: Simplified workflow deployment for anyone with basic Python knowledge; perfect for data management, ML model building, and more.
• Open Source Community: Benefit from a collaborative community that actively shares knowledge and improvements through an open PR process.

Apache Airflow Screenshots

Suggested Developer Use Cases

• Data Orchestration for Analytics: Low-code developers can leverage Airflow to design complex ETL pipelines that prepare data for analytics tools without writing extensive code.
• Scheduled Reporting: Create automated reports by integrating Airflow with business intelligence tools to deliver insights to stakeholders on a regular basis.
• MLOps Automation: Use Airflow to orchestrate machine learning workflows, managing tasks like model training, testing, deployment, and monitoring efficiently.