Apache-Airflow

Apache Airflow is an open-source platform used to programmatically author, schedule, and monitor workflows. It is widely used in data engineering to build complex, automated ETL (Extract, Transform, Load) pipelines where tasks and their dependencies are written as Python code.

• https://airflow.apache.org/

• https://github.com/apache/airflow

• Python

• workflow

• data-science

• machine-learning

• airflow

• automation

• etl

• workflow-engine

• scheduler

• apache

• orchestration

• data-engineering

• data-integration

• elt

• data-pipelines

• dag

• apache-airflow

• mlops

• workflow-orchestration

• data-orchestrator

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Key ConceptsDAGs (Directed Acyclic Graphs):

• The core concept of Airflow. A DAG represents a workflow and defines the tasks and their execution order, ensuring that tasks run sequentially without infinite loops.
• Operators: The building blocks of DAGs. Operators determine what actually happens when a task runs (e.g., executing a bash command, running a Python function, or triggering an API).
• Web UI: Airflow provides an intuitive, built-in dashboard. You can monitor pipeline health, view historical run logs, and manually trigger or retry failed tasks directly from the browse.

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Why Developers Use

• It Unlike simple scripts running on cron jobs, Airflow manages state, provides built-in failure recovery, and easily scales across distributed workers. Because workflows are defined in Python, they are easily version-controlled and highly dynamic.

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