Apache Airflow Reviews

Name: Apache Airflow
Brand: Apache
Rating: 4.0 (38 reviews)

4.0 out of 5

38 reviews
97% willing to recommend

717 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).

Get the Apache Airflow Buyer's Guide and find out what your peers are saying about Apache Airflow, Informatica Intelligent Data Management Cloud (IDMC), Camunda and more!

Apache Airflow is the #2 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 73% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a financial services firm, accounting for 25% of all views.

Helped 839,422 peers since 2012

Featured Apache Airflow reviews

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.…

Read full review

SUDHIR KUMAR RATHLAVATH

Student at University of South Florida

One improvement could be the inclusion of a plugin with a drag-and-drop feature. This graphical feature would be beneficial when dealing with connectivity and integration services like connecting to BigQuery or other systems. As a first-time user, although the documentation is available, it would be more user-friendly to have a drag-and-drop interface within the portal. Users could simply drag and drop components to create a pseudo-code, making it more flexible and intuitive. Therefore, I suggest having a drag-and-drop feature for a more user-friendly experience and better code management. Moreover, for admins, there should be improved logging capabilities because Apache Airflow does have logging, but it's limited to some database data. It would be better if everything goes into the server where it's hosted. Probably on the interface level. If something goes well for the developers.

Read full review

ManojKumar43

Big Data Engineer at BigTapp Analytics

Apache Airflow is easy to use and can monitor task execution easily. For instance, when performing setup tasks, you can conveniently view the logs without delving into the job details. All logs are readily accessible within the interface itself. Examining the logs lets you discern which steps and processes are being executed. You don't have to configure SMTP for everything. You need to configure email settings, such as email on error, failure, or alert access. With Apache Airflow, you can send emails with just a few lines of code. You don't have to write extensive code to configure SMTP; all those configurations can be accomplished within a few lines of code. I managed a complex workflow for a finance application project. They use Apache Airflow to orchestrate processes, such as retrieving data from SFTP and landing it into S3. From S3, they trigger Glue jobs based on certain conditions. Additionally, they use the Glue catalog in Glusoft for data management, all orchestrated using Airflow. Furthermore, various logics are written in Airflow DAGs to handle scenarios like security mismatches. For instance, files are sent accordingly if there's a missing security. Apache Airflow triggers a set of tasks based on DAGs. If you have multiple tags, such as raw, transform, and ready layers, instead of manually triggering each DAGs. In that case, you can integrate them to trigger one, automatically triggering the others. Also, you can put conditions.

Read full review

Apache Airflow mindshare

As of March 2025, the mindshare of Apache Airflow in the Business Process Management (BPM) category stands at 6.9%, down from 9.6% compared to the previous year, according to calculations based on PeerSpot user engagement data.

Business Process Management (BPM)

PeerAnalyst reports based on Apache Airflow reviews

Type	Title	Date
Category	Business Process Management (BPM)	Mar 4, 2025	Download
Product	Reviews, tips, and advice from real users	Mar 4, 2025	Download
Comparison	Apache Airflow vs Camunda	Mar 4, 2025	Download
Comparison	Apache Airflow vs Automation Anywhere	Mar 4, 2025	Download
Comparison	Apache Airflow vs Pega Platform	Mar 4, 2025	Download

Title	Rating	Mindshare	Recommending
Informatica Intelligent Data Management Cloud (IDMC)	3.9	1.2%	93%	182 interviews Add to research
Camunda	4.1	21.5%	89%	77 interviews Add to research

Valuable Features

Apache Airflow's most valuable features include its user-friendly interface, Python integration, and versatility in creating and managing workflows. It offers a graphical representation of pipeline status, facilitates monitoring, and supports multiple programming languages. The tool is praised for scalability, integration capabilities with various services, ease of use and automation, and extensive community support. Its flexibility allows customization and efficient orchestration of data workflows with comprehensive functionalities in automation and scheduling tasks.

"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."
"Apache Airflow is easy to scale and its UI improves with each release."

Room for Improvement

Apache Airflow lacks support for cyclic workflows and real-time job execution. Its UI and graphical capabilities require enhancement. Users find maintenance and integration complex, and versioning support is limited. Documentation and technical support need improvement. Scalability, cloud integration, and state management present challenges. Users suggest better platform compatibility, logging, and security features. There's a desire for visual scripting tools and more built-in connectors. Pricing and performance stability are also concerns.

"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."
"It is not suitable for real-time ETL tasks."

ROI

Users note Apache Airflow provides positive returns mainly due to its open-source nature and versatility. It efficiently facilitates tasks like data extraction, table summarization, and report generation. Many companies utilize its Python integration to enhance efficiency and ease of use, noting significant returns on investment. While some are still assessing precise data, most agree on its worthiness and timely pipeline creation abilities, ensuring good returns even with minimal initial investment details.

Pricing

Apache Airflow is open-source and free, with no licensing costs, providing a cost-effective option for enterprises. While users can deploy it locally or on cloud platforms like AWS, additional expenses may arise from infrastructure or managed services like Astronomer or Bitnami, which offer commercial support and extra features. For enterprises evaluating Apache Airflow, it remains a budget-friendly choice with optional costs for enhanced services and infrastructure deployment.

"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

Companies use Apache Airflow primarily for automating and orchestrating ETL processes, data pipelines, and workflow tasks in various industries, including technology, finance, and media. It assists in data transformation, scheduling, and model management, integrating with tools like DBT and Fivetran. Organizations deploy it in cloud and on-premises environments, supporting integration with SLAs, big data lakes, and machine learning, enabling efficient task automation and orchestration for diverse business workflows and data operations.

Service and Support

Apache Airflow offers open-source documentation and a strong community for support, often resolving issues without direct company assistance. Some rely on forums and resources like Stack Overflow, while others utilize internal support teams. Companies generally find technical assistance satisfactory, but some rate it highly. Many never contact the technical team, citing comprehensive guides and online resources as sufficient for problem-solving. Commercial support is available but often unnecessary.

Deployment

Apache Airflow's initial setup varies in complexity. Some find it intuitive and swift, especially with cloud setups via Google Cloud or AWS. Others encounter challenges with on-premises installations, requiring technical expertise. Documentation is praised, though additional case studies would be beneficial. Creative workarounds are sometimes needed. Experience with configurations is crucial, and while some rate the process highly, complexities may arise with cloud integrations or customized deployments. Many emphasize the importance of prior knowledge.

Scalability

Apache Airflow is known for its scalability capabilities, especially when configured as a cluster or deployed with Kubernetes. Users appreciate its adaptability for diverse requirements, with many successfully managing thousands of data pipelines. However, some encounter challenges with cloud-native integration, especially during high data loads. Scalability ranks generally high, though certain limitations arise in complex scenarios. Users utilize it across varied setups, valuing its flexibility despite occasional scaling complexities.

Stability

Users report mixed experiences with Apache Airflow's stability. Many find it stable, giving ratings between seven and ten out of ten. Some encounter bugs, glitches, and performance issues, particularly during heavy task loads or complex configurations. Community support is essential for troubleshooting due to open-source limitations. Performance is seen as satisfactory, especially when deployed on-premises or with Kubernetes. Examples of stability challenges include issues with large-scale operations and setup difficulties.

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

25%

Computer Software Company

14%

Manufacturing Company

Retailer

Healthcare Company

Insurance Company

Educational Organization

Comms Service Provider

Government

Energy/Utilities Company

University

Media Company

Real Estate/Law Firm

Non Profit

Hospitality Company

Construction Company

Recreational Facilities/Services Company

Pharma/Biotech Company

Logistics Company

Legal Firm

Wholesaler/Distributor

Consumer Goods Company

Transportation Company

Outsourcing Company

Performing Arts

Aerospace/Defense Firm

Engineering Company

Marketing Services Firm

Compare Apache Airflow with alternative products

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.