Apache Airflow is an open-source workflow management system (WMS) that is primarily used to programmatically author, orchestrate, schedule, and monitor data pipelines as well as workflows. The solution makes it possible for you to manage your data pipelines by authoring workflows as directed acyclic graphs (DAGs) of tasks. By using Apache Airflow, you can orchestrate data pipelines over object stores and data warehouses, run workflows that are not data-related, and can also create and manage scripted data pipelines as code (Python).
| Product | Market Share (%) |
|---|---|
| Apache Airflow | 6.0% |
| Camunda | 19.1% |
| SAP Signavio Process Manager | 7.6% |
| Other | 67.3% |
| Type | Title | Date | |
|---|---|---|---|
| Category | Business Process Management (BPM) | Aug 28, 2025 | Download |
| Product | Reviews, tips, and advice from real users | Aug 28, 2025 | Download |
| Comparison | Apache Airflow vs Camunda | Aug 28, 2025 | Download |
| Comparison | Apache Airflow vs Automation Anywhere | Aug 28, 2025 | Download |
| Comparison | Apache Airflow vs SAP Signavio Process Manager | Aug 28, 2025 | Download |
| Title | Rating | Mindshare | Recommending | |
|---|---|---|---|---|
| Camunda | 4.1 | 19.1% | 89% | 77 interviewsAdd to research |
| Informatica Intelligent Data Management Cloud (IDMC) | 4.0 | 1.3% | 93% | 185 interviewsAdd to research |
| Company Size | Count |
|---|---|
| Small Business | 12 |
| Midsize Enterprise | 3 |
| Large Enterprise | 19 |
| Company Size | Count |
|---|---|
| Small Business | 161 |
| Midsize Enterprise | 114 |
| Large Enterprise | 672 |
Apache Airflow Features
Apache Airflow has many valuable key features. Some of the most useful ones include:
Apache Airflow Benefits
There are many benefits to implementing Apache Airflow. Some of the biggest advantages the solution offers include:
Reviews from Real Users
Below are some reviews and helpful feedback written by PeerSpot users currently using the Apache Airflow solution.
A Senior Solutions Architect/Software Architect says, “The product integrates well with other pipelines and solutions. The ease of building different processes is very valuable to us. The difference between Kafka and Airflow, is that it's better for dealing with the specific flows that we want to do some transformation. It's very easy to create flows.”
An Assistant Manager at a comms service provider mentions, “The best part of Airflow is its direct support for Python, especially because Python is so important for data science, engineering, and design. This makes the programmatic aspect of our work easy for us, and it means we can automate a lot.”
A Senior Software Engineer at a pharma/biotech company comments that he likes Apache Airflow because it is “Feature rich, open-source, and good for building data pipelines.”
Apache Airflow was previously known as Airflow.
Agari, WePay, Astronomer
| Author info | Rating | Review Summary |
|---|---|---|
| Head of Data at a energy/utilities company with 51-200 employees | 4.0 | We use Apache Airflow on Google to manage machine learning pipelines, benefiting from job resumption, error logs, and notifications, though it's limited in external integration options. The ROI is indirect and depends on other tools involved. |
| Team Lead, Data Engineering at Nesine.com | 4.5 | I use Apache Airflow to orchestrate jobs and manage batch ETL processes effectively, benefiting from its scalability and UI improvements. Although not ideal for real-time tasks, it outperforms CronJob in log tracking and task management. |
| Sr. Team Lead - IT at InfoStretch | 5.0 | We use Apache Airflow for its modular architecture and integration capabilities to build machine learning models, transport data, and manage infrastructure with ease. While scalable and easy to maintain, it could benefit from a dashboard for better workflow analysis. |
| Senior Software Engineer at Annalect India | 4.5 | I find Apache Airflow valuable because it is an open-source tool that integrates well with any cloud environment like AWS, aiding in orchestration and team notifications. However, its handling of Python package dependencies during tests needs improvement. |
| Data Engineer III at a tech consulting company with 10,001+ employees | 4.5 | I've used Apache Airflow for three years to orchestrate data pipelines and reports. Its Python-based structure and UI are great, though task reruns and start dates are confusing and could benefit from clearer documentation and simplification. |
| Senior Data Engineer at a consultancy with 10,001+ employees | 4.5 | We primarily use Apache Airflow for ETL pipelines and scheduling automation jobs. Its intuitive UI and powerful Python declarative language minimize the learning curve. While the UI could be modernized, its strong core features offer a good ROI. |
| Product Owner at La Poste S.A. | 3.5 | We primarily use Apache Airflow for complex ETL tasks, benefiting from its extensive documentation and resources. However, improved automation, a visual workflow designer, and graphical tools would enhance usability, although it meets our needs being open-source and standard worldwide. |
| Program Python at Santander Bank Polska | 4.5 | In my company, we use Apache Airflow for orchestrating automation tasks because it's versatile and Python-based, making it easy to learn. However, it lacks integration with Oracle databases, which is crucial for many production environments. |
