---
title: "How Airflow Orchestrates Data Pipelines"
description: "Airflow is a workflow orchestration platform that schedules, monitors, and manages data pipelines by defining tasks as code, executing them in dependency order, and tracking their success or failure."
---

# How Airflow Orchestrates Data Pipelines

**Type:** [How it works](https://yomu.fyi/visual-explainers/category/how-it-works.md)

![Left-to-right flow showing DAG definition in Python, scheduler evaluation, task execution with dependencies, and monitoring dashboard with status and logs.](https://yomufyi.s3.us-east-1.amazonaws.com/visual-explainers/93d8cbad-4d2e-4ffc-af4b-7375674d04b8/image-035c282b-645a-4e8a-87ff-dbd55d5da620.webp)

> Airflow is a workflow orchestration platform that schedules, monitors, and manages data pipelines by defining tasks as code, executing them in dependency order, and tracking their success or failure.

## Explanation

Apache Airflow lets engineers define complex data workflows as directed acyclic graphs (DAGs) where each node is a task and edges represent dependencies. A scheduler evaluates DAG definitions, triggers tasks when their upstream dependencies complete, and a web UI shows real-time execution status and historical runs. Airflow integrates with external systems through operators and sensors, handles retries and failure callbacks, and provides visibility into pipeline health—making it essential for teams managing hundreds of interdependent data jobs across cloud and on-premises infrastructure.

**Tags:** [Airflow](https://yomu.fyi/topic/airflow.md), [CI/CD](https://yomu.fyi/topic/ci-cd.md), [Data Engineering](https://yomu.fyi/topic/data-engineering.md), [Data Pipelines](https://yomu.fyi/topic/data-pipelines.md), [Monitoring](https://yomu.fyi/topic/monitoring.md)

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