Apache Airflow Reviews

Name: Apache Airflow
Brand: Apache
Rating: 4.0 (39 reviews)

4.0 out of 5

39 reviews
97% willing to recommend

491 followers

What is Apache Airflow?

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).

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Apache Airflow is the #3 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 72% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a financial services firm, accounting for 27% of all views.

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Featured Apache Airflow reviews

Madhan Potluri

Head of Data at a energy/utilities company with 51-200 employees

We utilize Apache Airflow for managing machine learning production pipelines. The deployment of our machine learning models takes place through Apache Airflow. We configure pipelines, decide on the frequency of recalibration, deployment, and dataset training Apache Airflow allows us to resume…

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Kemal Duman

Team Lead, Data Engineering at Nesine.com

I am using Apache Airflow to orchestrate my jobs, projects, and batch ETL jobs. It manages tasks, orchestrates jobs, and helps in handling batch processes effectively, incorporating integrations like DBT and Great Expectations Apache Airflow is easy to scale and its UI improves with each release.…

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Prathamesh D Marathe

Senior Software Engineer at Annalect India

DAG or a directed acyclic graph functionality has enhanced our company's workflow management since it helps to find out the source tasks that are running and also the target that fall subsequent to them, and it helps figure out how the data flow is working. In DAG, our company can group the tasks, and so that helps to figure out which group of tasks are running. Speaking about my experience with Apache Airflow's UI for monitoring and managing workflows, I would say that the tool's UI allows one to add variables, and it also allows one to check the status of the tasks that are running or the previous run on a particular DAG. Our company can send notifications via Apache Airflow, and also check the connection and configuration details. I recommend the tool to others who plan to use it since it is quite easy to use and it is easy to implement its functionalities for the use cases. I rate the tool a nine out of ten.

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Apache Airflow mindshare

As of June 2025, the mindshare of Apache Airflow in the Business Process Management (BPM) category stands at 6.3%, down from 8.9% compared to the previous year, according to calculations based on PeerSpot user engagement data.

Business Process Management (BPM)

PeerResearch reports based on Apache Airflow reviews

Type	Title	Date
Category	Business Process Management (BPM)	Jun 15, 2025	Download
Product	Reviews, tips, and advice from real users	Jun 15, 2025	Download
Comparison	Apache Airflow vs Camunda	Jun 15, 2025	Download
Comparison	Apache Airflow vs Automation Anywhere	Jun 15, 2025	Download
Comparison	Apache Airflow vs SAP Signavio Process Manager	Jun 15, 2025	Download

Title	Rating	Mindshare	Recommending
Informatica Intelligent Data Management Cloud (IDMC)	4.0	1.1%	93%	185 interviews Add to research
Camunda	4.1	20.5%	89%	77 interviews Add to research

Valuable Features

Apache Airflow's most valuable features include its intuitive UI, Python integration, and scalability. Users appreciate the DAG presentation for complex workflows, ease of automation, and extensive community support. It integrates well with cloud platforms and other services, offering flexible customization. It simplifies designing, scheduling, and monitoring workflows, supports rapid development, and enhances connectivity. Its capability to manage and track data pipelines efficiently is highly appreciated, making it a popular choice among data engineers.

"Apache Airflow is a stable and flexible platform."
"I definitely recommend Apache Airflow and would rate it nine out of ten."
"I would rate Apache Airflow from one to ten points overall as ten points, which means it is a very good solution."

Room for Improvement

Apache Airflow users seek improvements in integration flexibility, user interface, and automated components. Many report struggles with scalability, maintenance, and versioning. Users express a need for enhanced graphical features for workflow design and real-time job support. Documentation quality and technical support are areas of concern. Compatibility with diverse programming languages and databases is desired. There are calls for better resource management, drag-and-drop capabilities, and broader cloud platform integration.

"Currently, Apache Airflow is closely coupled, limiting the ability to link to outside channels by ourselves."
"Running frequent jobs, such as every minute or five minutes, is not appropriate for Airflow. It is not suitable for real-time ETL tasks."
"There is a minor issue with the manual work in Airflow, as everyday activities are managed manually. There is no dashboard for us to check all the Directed Acyclic Graphs (DAGs); a dashboard would help us analyze the work better."

ROI

Several entities find Apache Airflow generates a positive return due to its open-source status and utility. Many appreciate its ability to handle diverse tasks efficiently, enhancing their high-level tasks with Python integration. In contrast, some see no benefit as similar tasks can be completed with other resources. A few report Apache Airflow isn't solely accountable for returns because it works alongside other systems. Overall satisfaction relates to its capability to construct swift data pipelines.

Pricing

Apache Airflow is an open-source, free solution, making it cost-effective for enterprises. Users only incur operational costs for infrastructure like virtual machines. Although community support is available, commercial support from providers such as Astronomer or Bitnami incurs additional charges. Many find it affordable and prefer the community edition over enterprise versions to manage expenses. Enterprises deploying in cloud environments should consider costs associated with services like Amazon AWS, Google Composer, and Azure's managed Airflow solutions.

"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

Organizations employ Apache Airflow primarily for data workflow automation and orchestration. It facilitates ETL processes, data transformation, and pipeline scheduling, supporting various industries like finance, media, and technology. Its capabilities extend to machine learning model deployment, database data ingestion, and BI operations. Users appreciate its versatility in managing hybrid cloud infrastructures and integrating with other tools for efficient data processing and task automation across different environments and platforms.

Service and Support

Apache Airflow's support is primarily community-driven, with users often consulting online resources like GitHub and Stack Overflow. Companies report mixed experiences, with some expressing high satisfaction, referring to the documentation and community as very helpful, while others rely on internal teams or find third-party or community support necessary. Several users mention the extensive online resources available, which aids in self-solving technical issues, sometimes without needing direct contact with Apache's support teams.

Deployment

Apache Airflow's initial setup varies in complexity. Some find it straightforward, manageable in minutes with cloud services, while others describe it as intricate due to multiple components and customization needs. Documentation is helpful, though lacking some details. Engineers often manage deployments, utilizing cloud platforms or private installations. Experience with containers and specific tools is beneficial. Challenges include integration with data sources, scaling issues, and a need for case studies from other companies.

Scalability

Apache Airflow is highly adaptable, with many teams leveraging its ability to handle diverse user groups and environments. While it integrates well with Kubernetes for enhanced scalability, challenges exist in cloud-native environments and for scaling beyond certain limits. Users often find it effective for handling numerous tasks up to several thousand but acknowledge limitations in extreme cases. Multiple teams appreciate its lightweight nature, allowing for expansion based on needs without significant performance concerns.

Stability

Opinions vary on Apache Airflow's stability. Many find it stable, rating it between seven to ten out of ten, while some experience occasional glitches and challenges with on-premises configurations. Configurations and support influence stability, with users noting some bugs and the need for community support for troubleshooting. Performance is often satisfactory, but issues may occur when too many tasks run simultaneously. Despite some problems, many users describe it as reliable and stable.

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

By visitors reading reviews

Top industries

By visitors reading reviews

Financial Services Firm

27%

Computer Software Company

14%

Manufacturing Company

Retailer

Healthcare Company

Educational Organization

Comms Service Provider

University

Government

Insurance Company

Media Company

Energy/Utilities Company

Real Estate/Law Firm

Non Profit

Construction Company

Hospitality Company

Pharma/Biotech Company

Legal Firm

Recreational Facilities/Services Company

Consumer Goods Company

Logistics Company

Performing Arts

Wholesaler/Distributor

Outsourcing Company

Aerospace/Defense Firm

Transportation Company

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Learn more about Apache Airflow

Apache Airflow Features

Apache Airflow has many valuable key features. Some of the most useful ones include:

Smart sensor: In Apache Airflow, tasks are executed sequentially. The smart sensors are executed in bundles, and therefore consume fewer resources.

Dockerfile: By using Apache Airflow’s dockerfile feature, you can run your business’s Airflow code without having to document and automate the process of running Airflow on a server.

Scalability: Because Apache Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers, you can easily scale it.

Plug-and-play operators: With Apache Airflow, you can choose from several plug-and-play operators that are ready to execute your tasks on many third-party services.

Apache Airflow Benefits

There are many benefits to implementing Apache Airflow. Some of the biggest advantages the solution offers include:

User friendly: Using Apache Airflow requires minimal python knowledge to get started.

Intuitive user interface: The Apache Airflow user interface enables you to visualize pipelines running in production, monitor progress, and also troubleshoot issues when needed.

Easy integration: Apache Airflow can easily be integrated with cloud platforms (Google, AWS, Azure, etc).

Visual DAGs: Apache Airflow’s visual DAGs provide data lineage, which facilitates debugging of data flows and also aids in auditing and data governance.

Flexibility: Apache Airflow provides you with several ways to make DAG objects more flexible. At runtime, a context variable is passed to each workflow execution, which is quickly incorporated into an SQL statement that includes the run ID, execution date, and last and next run times.

Multiple deployment options: With Apache Airflow, you have several options for deployment, including self-service, open source, or a managed service.

Several data source connections: Apache Airflow can connect to a variety of data sources, including APIs, databases, data warehouses, and more.

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.