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Apache Airflow Reviews

Vendor: Apache
4.0 out of 5
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What is Apache Airflow?

Apache Airflow is a Python-based platform that simplifies task scheduling, workflow orchestration, and monitoring of ETL processes with a user-friendly UI and integration capabilities.

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Apache Airflow is the #4 ranked solution in BPM Software. PeerSpot users give Apache Airflow an average rating of 8.0 out of 10. Apache Airflow is most commonly compared to Informatica Intelligent Data Management Cloud (IDMC): Apache Airflow vs Informatica Intelligent Data Management Cloud (IDMC). Apache Airflow is popular among the large enterprise segment, accounting for 64% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a financial services firm, accounting for 21% of all views.
Buyer's Guide
Apache Airflow
April 2026
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Apache Airflow mindshare

As of May 2026, the mindshare of Apache Airflow in the Business Process Management (BPM) category stands at 2.8%, down from 6.4% compared to the previous year, according to calculations based on PeerSpot user engagement data.
Business Process Management (BPM) Mindshare Distribution
ProductMindshare (%)
Apache Airflow2.8%
Camunda7.7%
IBM BPM4.1%
Other85.4%
Business Process Management (BPM)

PeerResearch reports based on Apache Airflow reviews

TypeTitleDate
CategoryBusiness Process Management (BPM)May 2, 2026Download
ProductReviews, tips, and advice from real usersMay 2, 2026Download
ComparisonApache Airflow vs CamundaMay 2, 2026Download
ComparisonApache Airflow vs Automation AnywhereMay 2, 2026Download
ComparisonApache Airflow vs Pega PlatformMay 2, 2026Download
Suggested products
TitleRatingMindshareRecommending
Informatica Intelligent Data Management Cloud (IDMC)4.01.7%92%214 interviewsAdd to research
Camunda4.17.7%89%78 interviewsAdd to research
 
 
Key learnings from peers
Last updated Mar 1, 2026

Valuable Features

Apache Airflow's most valuable features include flexibility and scalability with Python-based DAG creation, rich integrations, event-based scheduling, and a comprehensive UI. Its open-source nature attracts global contributions enhancing its tech stack. Users benefit from frequent updates, customizable workflows, and connectivity with various services. The user-friendly interface simplifies monitoring and debugging. Its vast community support and extensive documentation ease learning and integration, making it popular for data orchestration and workflow management.
  • "It helped us to manage the complex workflows and orchestration of tasks efficiently."
  • "I love this solution."
  • "Apache Airflow's best feature is its flexibility."

Room for Improvement

Apache Airflow's improvements are needed in UI experience, scheduler stability, real-time job support, and integration with various databases and platforms. Enhanced documentation, streamlined onboarding, and better logging are crucial for users. The lack of visual workflow designers and the complexity of maintenance also require attention. Users find challenges with scalability, state management, and the absence of out-of-the-box features for hybrid setups. Improvements in manual task handling, interface aesthetics, and technical support access are also necessary.
  • "UI can be improved with additional user-friendly features for non-programmers and for fewer coding practitioner requirements."
  • "I am using a Celery Executor and I find that it crashes and I can't see any logs."
  • "Apache Airflow could be improved with the addition of more frameworks."

ROI

Many found Apache Airflow valuable, noting its high ROI due to its open-source nature and utility across tasks like data pulling and report generation, with Python integration enhancing ease of use. Some organizations built efficient pipelines quickly, boosting perceived return. Others awaited precise data or found minimal benefit, indicating mixed experiences. While widespread adoption suggests high returns, some mentioned difficulty in isolating Apache Airflow's specific financial impact due to other involved tools.

Pricing

Apache Airflow is an open-source solution with no licensing fees, making it cost-effective for enterprises. While the software itself is free, operational costs such as infrastructure, particularly when using cloud services like AWS or EC2, must be considered. Commercial support and managed services are available via providers like Astronomer and Bitnami, which incur additional costs. Many users prefer the community edition to control expenses, while some opt for managed services for ease of setup and additional features.
  • "Apache Airflow is open-source and free. Hyperscalers like Google (with Composer), Azure, and AWS offer managed Airflow services."
  • "I use the tool's open-source version."
  • "It is an open-source tool. There are no additional fees or charges associated with the product."

Popular Use Cases

Apache Airflow is primarily used for orchestration and automation of data pipelines and ETL processes. Users employ it for data ingestion, curation, and transformation in both cloud and on-premises environments. It supports scheduling tasks, integration with various data sources, and managing complex workflows. Teams often utilize Airflow in conjunction with tools like Spark, dbt, and Fivetran, facilitating business intelligence, machine learning, and data engineering operations across diverse industries.

Service and Support

Apache Airflow customer service and support involve community and documentation reliance, as it is open-source. Many users address issues themselves using resources like Stack Overflow and GitHub. While some have contacted developers or found technical support satisfactory, others manage without direct interaction. Numerous users benefit from online information and a robust community, though some companies could offer paid support. Several find satisfaction from their internal support teams or external third-party services.

Deployment

Apache Airflow's initial setup varies in complexity. Some find the cloud deployment straightforward, especially with tools like AWS and Cloud Composer, while others note challenges with on-premises and Kubernetes configurations. Documentation is often adequate but sometimes lacks detail. Engineers commonly participate in the setup, citing a learning curve and integration challenges. Despite some difficulties, once familiar, users manage easier subsequent deployments.

Scalability

Apache Airflow is scalable, leveraging Kubernetes for dynamic scaling and handling thousands of tasks efficiently. While some find it robust, others face challenges with high-volume data and cloud-native integration. Its user base ranges significantly, illustrating its broad adaptability. Users note potential limitations but appreciate its functionality within Python. Scalability issues surface with extensive pipelines, yet adding computing capabilities and configuring executives like KEDA enhances performance. Transitioning to a cluster can alleviate certain constraints.

Stability

Many agree that Apache Airflow is stable for most use cases, but expertise is needed for large-scale projects. While stability is praised, some configurations can lead to occasional issues and errors. Several users rate its stability highly, describing it as 99% stable with few glitches. On-premises deployment is often seen as more stable than cloud options. Users highlight performance and scalability as strong points, yet some experience bugs and lack of support solutions.
These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.
Download our Apache Airflow Buyer's Guide for additional reliable information.

Review data by company size

By reviewers
Company SizeCount
Small Business12
Midsize Enterprise4
Large Enterprise19
By reviewers
By visitors reading reviews
Company SizeCount
Small Business142
Midsize Enterprise70
Large Enterprise384
By visitors reading reviews

Top industries

By visitors reading reviews
Financial Services Firm
21%
Computer Software Company
11%
Manufacturing Company
8%
Comms Service Provider
7%
Insurance Company
6%
Outsourcing Company
4%
Construction Company
4%
Healthcare Company
4%
Retailer
3%
University
3%
Government
3%
Performing Arts
2%
Media Company
2%
Non Profit
2%
Hospitality Company
2%
Energy/Utilities Company
2%
Educational Organization
2%
Legal Firm
1%
Logistics Company
1%
Marketing Services Firm
1%
Recreational Facilities/Services Company
1%
Real Estate/Law Firm
1%
Consumer Goods Company
1%
Transportation Company
1%
Aerospace/Defense Firm
1%
Pharma/Biotech Company
1%

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Learn more about Apache Airflow

Apache Airflow facilitates workflow automation through its open-source framework, offering extensive customization and scalability. Users benefit from its visual DAG representation, event-based scheduling, and task retry functionality. Frequent updates and rich integration features allow seamless interaction with platforms like AWS and Google Cloud, while Python-friendly configurations enable robust error handling and notifications. Despite requiring improvements in integration and documentation, its application spans industries such as technology, finance, and entertainment, supporting tasks like data ingestion and synchronization.

What are the key features of Apache Airflow?
  • User-Friendly UI: Simplifies monitoring and management of ETL tasks.
  • Visual DAG Representation: Offers clear visualization of task dependencies.
  • Event-Based Scheduling: Automates workflow actions based on specific triggers.
  • Task Retry Capabilities: Enhances reliability through automatic task retries.
  • Python-Based Customization: Allows flexible and extensive feature customization.
  • Open-Source: Supports continuous updates and integration capabilities.
What benefits should users consider in judging Apache Airflow?
  • Workflow Automation: Streamlines and enhances efficiency in process execution.
  • Error Handling: Improves task reliability with effective error management.
  • Integration with Cloud Platforms: Enhances flexibility with AWS and Google Cloud compatibility.
  • Scalability: Supports growing infrastructural needs with ease.

Apache Airflow's deployment in industries like technology, finance, and entertainment is primarily focused on automating ETL processes, managing media workflows, and orchestrating data transformation tasks. It effectively integrates with tools such as SQL scripts and Databricks, enabling organizations to manage data pipelines efficiently in both cloud and on-premises environments.

Apache Airflow was previously known as Airflow.

Apache Airflow customers

Agari, WePay, Astronomer

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Product Categories

Product Categories
Business Process Management (BPM)

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Apache Airflow Reviews Summary
Author infoRatingReview Summary
Administrator at a tech vendor with 201-500 employees3.0I use Apache Airflow for ingestion and curation pipelines across many sources into Hive on S3, triggering Spark jobs. I value its open-source flexibility and integrations, but want better UI/scheduler stability; it suits small-scale use with expertise.
Head of Data at a energy/utilities company with 51-200 employees4.0We 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.
Data Engineer III at a tech consulting company with 10,001+ employees4.5I'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.
Team Lead, Data Engineering at Nesine.com4.5I 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 InfoStretch5.0We 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 India4.5I 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.
Student at University of South Florida5.0Apache Airflow efficiently orchestrates data workflows, offering valuable features like Python operator integration and ease of use. Despite high ROI and adoption, improvements in drag-and-drop interfaces and enhanced logging could enhance its user-friendliness and integration capabilities.
Senior Data Engineer at a consultancy with 10,001+ employees4.5We 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.