Apache Airflow
Apache Airflow is an open-source platform for programmatically authoring, scheduling, and monitoring data pipelines and batch workflows.
Apache Airflow is a scalable, extensible workflow orchestration platform that empowers teams to programmatically author, schedule, and monitor complex data pipelines. Created by the open-source community, Airflow is battle-tested at organizations of every size.
Key Features:
- Pure Python - Define workflows as code using standard Python, no XML or CLI hacks
- Robust Web UI - Monitor, schedule, and manage workflows with a modern dashboard
- Extensible Operators - Plug-and-play integrations with AWS, GCP, Azure, and hundreds more
- Dynamic Pipelines - Generate pipeline DAGs dynamically with Python loops and parameters
- Scalable Architecture - Modular message queue design scales to distribute tasks with supported executors
Whether you're a data engineer, ML practitioner, or scraping team lead, Airflow provides the foundation to automate and monitor any pipeline.
Web scraping workflow
How to use Apache Airflow in a web scraping pipeline
Apache Airflow coordinates the stages around a recurring crawl using tasks and their dependencies. It is useful when fetching, validation, loading and reporting need a visible execution order rather than one large scraper script.
A practical workflow
- Start a DAG with a dated URL manifest and a storage location for this run.
- Run your Scrapy or Playwright program in a fetch task, then let dependent tasks validate the resulting records and load accepted data.
- Publish a dataset manifest only after required tasks succeed; keep raw captures available for investigating failures.
When it fits
Use it for scheduled batch collection with downstream warehouse or reporting jobs. Passing file locations between tasks keeps large HTML bodies out of orchestration metadata and makes individual stages easier to rerun.
Boundaries
Airflow provides orchestration, not a browser or extraction rules. Configure worker dependencies, credentials, request pacing and idempotent writes yourself. A completed task shows that its program finished; it does not prove the extracted fields are correct or the target allowed collection.
Quick verdict
Apache Airflow is for teams that need Python-based workflow orchestration around scraping, data, or ML pipelines. It is not a scraper or extraction API by itself.
- Best for
- Python workflow orchestration
- Scheduled scraping pipelines
- Retries and dependency management
- Open-source data teams
- Not ideal for
- Point-and-click scraping
- Standalone browser automation
- Simple one-off scripts
- Last verified
- Jun 26, 2026
Key facts
Growing a website? Track its keywords, Google rankings, Search Console insights, and share of voice with bisibility. Free during beta.
Our productRepository
apache/airflow
Stars
47.2KForks
18KLast commit
2026-10-15License
Apache-2.0Language
Python
Metrics recorded 2026-10-16
Repository details
License source: Repository license file
Frequently asked questions
What is Apache Airflow used for?
Airflow defines, schedules and monitors workflows as Python code, including batch-oriented data pipelines. Tasks and their dependencies form DAGs, and the scheduler coordinates execution. Workflows can use time-based or event-driven triggers.
Sources (checked ): Apache Airflow introduction; Airflow architecture
Is Airflow free to use?
Apache Airflow is open source under Apache 2.0, with no project subscription fee. Running its scheduler, database and workers brings infrastructure and maintenance costs. Third-party managed Airflow services have separate pricing.
Sources (checked ): Airflow Apache 2.0 license; Airflow architecture
What is the difference between Kafka and Airflow?
Kafka publishes, stores and processes streams of events. Airflow schedules and monitors workflow tasks and dependencies. An Airflow task can interact with Kafka, but the scheduler and the event-streaming platform perform different roles.
Sources (checked ): Apache Airflow introduction; Kafka introduction
Can dbt replace Airflow?
dbt transforms data in a data platform and manages model dependencies. Airflow orchestrates workflows that can include dbt runs alongside ingestion or other tasks. Whether dbt alone is sufficient depends on the wider workflow you need to schedule.
Sources (checked ): Apache Airflow introduction; dbt framework
Sources
Verified against the apache/airflow repository and the PyPI package.
Source notes
Compiled from public repository and package data; check the Apache Airflow site for current details.