Find out what your peers are saying about Amazon Web Services (AWS), Informatica, Salesforce and others in Cloud Data Integration.
Forums and community resources like Stack Overflow are helpful.
There is enough documentation available, and the community support is good.
The solution is very scalable.
Apache Airflow scales well, especially when deployed in Kubernetes environments.
Vertically, scalability is fine, however, I have not expanded horizontally with the product yet.
Apache Airflow is stable and I have not experienced significant issues.
I would rate the stability of the solution as ten out of ten.
There are some issues like the tool hanging or the need for additional jars when exposing web services.
It is not suitable for real-time ETL tasks.
There is no dashboard for us to check all the Directed Acyclic Graphs (DAGs); a dashboard would help us analyze the work better.
A special discount of at least 50% for old customers would allow us to expand our services and request more resources.
I prefer using the open-source version rather than the enterprise version, which helps manage costs.
Apache Airflow is a community-based platform and is not a licensed product.
Reliability is good, and when integrated with Kubernetes, it performs better compared to on-premises environments.
Apache Airflow is an open-source platform that allows easy integration with AWS, Azure, and Google Cloud Platform.
It facilitates the exposure of around 235 services through our platform to feed various government entities across the entire country.
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).
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.”
webMethods.io Integration is a powerful integration platform as a service (iPaaS) that provides a combination of capabilities offered by ESBs, data integration systems, API management tools, and B2B gateways.
We monitor all Cloud Data Integration reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
