Introduction to Apache Airflow

What is Apache Airflow?

Airflow is an open‑source platform that allows users to programmatically author, schedule, and monitor workflows using Directed Acyclic Graphs (DAGs). Initiated by Airbnb in 2014, it has attracted around 800 contributors and over 13,000 stars on GitHub.

It is used by more than 200 companies, including Airbnb, Yahoo, PayPal, Intel, and Stripe, to manage data‑pipeline workflows.

What is a Workflow?

A workflow is a sequence of tasks that can be started on a schedule or triggered by an event, commonly used for big‑data processing pipelines.

A typical workflow diagram

Workflows generally consist of five phases: data download, data transfer for processing, execution of processing, result collection, and report generation (often emailed).

Working of Apache Airflow

Airflow’s architecture includes four main components:

Scheduler: Monitors all DAGs and their tasks, periodically checking for tasks to initiate.

Web Server: Provides a user interface to view job status, interact with databases, and read logs from remote storage such as Google Cloud Storage or Azure Blob.

Database: Stores DAG and task state metadata using SQLAlchemy and ORM.

Executor: Executes tasks; several executors are available: SequentialExecutor – runs one task at a time, useful for testing. LocalExecutor – enables parallelism on a single machine or node. CeleryExecutor – preferred for distributed Airflow clusters. KubernetesExecutor – creates temporary pods via the Kubernetes API for each task instance.

How does Airflow work?

The scheduler periodically scans the DAGs, creates task instances for pending tasks, and updates their status in the metadata database. Tasks are queued and picked up by workers, transitioning through states such as SCHEDULED, QUEUED, RUNNING, and finally SUCCESS or FAILED.

Features

Easy to Use: Requires only basic Python knowledge to deploy workflows.

Open Source: Free with an active community.

Robust Integrations: Provides ready‑to‑use operators for Google Cloud, AWS, Azure, etc.

Standard Python: Allows creation of simple to complex workflows using pure Python.

Amazing User Interface: Enables monitoring and management of workflow status.

Principles

Dynamic: Pipelines are defined as code, allowing dynamic generation.

Extensible: Users can define custom operators, executors, and extend the library.

Elegant: Pipelines are lean and explicit.

Scalable: Modular architecture with message‑queue orchestration supports unlimited scaling.