Apache Airflow Reviews - Pros & Cons, Ratings & more

Airtable

Bitrix24

monday.com

Jenkins

Apache Airflow is a platform created by the community to programmatically author, schedule, and monitor workflows. It has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers, making it scalable. Apache Airflow pipelines are defined in Python, allowing for dynamic pipeline generation and flexibility when building workflows.

Apache Airflow

Web-based

Data Analytics Manager

I like the whole concept of DAGs which was made prominent, I think, by Airflow in the data engineering workspace. Airflow is really good at this, both in executing and also in monitoring.

Engineering manager 

The main problem in this SW is the communication that fails often.

Data scientist

Great UI; good integration with others; just python knowledge is required.

Consultant II

But what I like the most is that it is open-source and also allows us to define workflows as code, which makes it easy to maintain, version control, and share with others.

Data aengineer

There is no task level notification anything fails. Also need integration with tools like slack for failure notifications.

University Intern

Some users have reported issues when setting up Airflow in different environments, which can result in stability or performance problems.

BigData Engineer

The best workflow management tool that is loaded with features.

Senior Developer

The UI was also a pleasant upgrade from our CLI-based task scheduling system.

Airflow can be difficult to learn and properly configure, especially for those who do not have prior experience in programming or server administration.

The SW helps and easy to use, no special training requires for using it.

Apache Oozie

Apache Airflow Review

A stable open source product solution to manage and execute batch jobs.

Deploy/schedule/manage DAG's easily with Airflow

Apache Airflow is a highly useful platform for orchestrating and coordinating complex data processing workflows. While there may be a steep learning curve and some configuration issues, the benefits in terms of flexibility, scalability, and monitoring make it a valuable tool for many data and analytics teams.

Take control of your data processing workflows with Apache Airflow.

Airflow is a well known and well implemented scheduler that can be use by developpers but also other non IT collabs.

Review of Airflow

Airflow solved several problems for our business, namely notifications and retry behavior. In addition, it offered integrations with several services that lowered the amount of development time or knowledge we needed. We have since transitioned most of our standalone scripts to this system simply because it does everything we have needed it to.

Review of Apache Airflow

Overall, Airflow has allowed us to develop and maintain a large number of automated workflows, both for data engineering and data science. It gave us a force multiplier for each data engineering hire which makes our team a lot more efficient compared to before.

Powerful workflow orchestration tool

It deals with the challenge of managing processes and acts as a bridge to allow data to move from the source to the extremely dynamic destination without a lot of human intervention, allowing for straightforward problem-solving when it occurs.

Apache Airflow Reviews

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Pros and cons

11 reviews