One of its most valuable features is the graphical user interface, providing a visual representation of the pipeline status, successes, failures, and informative developer messages.
Every feature in Apache Airflow is valuable. The number of operators and features I've used are mainly related to connectivity services and integrated services because I primarily work with GCP.
Apache Airflow is a great orchestration and automation tool. Its connectivity with other systems is a great plus point. The interactive UI, the options for scheduling and the very fact that its compatibility with Python.
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.
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...
The tool is user-friendly.
Since it's widely adopted by the community, Apache Airflow is a user-friendly solution.
We're running it on a virtual server, which we can easily upgrade if needed.
One of its most valuable features is the graphical user interface, providing a visual representation of the pipeline status, successes, failures, and informative developer messages.
The solution's UI allows me to collect all the information and see the code lines.
The product is stable.
Every feature in Apache Airflow is valuable. The number of operators and features I've used are mainly related to connectivity services and integrated services because I primarily work with GCP.
Since the solution is programmatic, it allows users to define pipelines in code rather than drag and drop.
Since Apache works very well on Python, we can manage everything and create pipelines there.
The most valuable feature of Apache Airflow is creating and scheduling jobs. Additionally, the reattempt at failed jobs is useful.
The solution is quite configurable so it is easy to code within a configuration kind of environment.
Designing processes and workflows is easier, and it assists in coordinating all of the different processes.
Apache Airflow's best feature is its flexibility.
The best feature is the customization.
The solution is flexible for all programming languages for all frameworks.
I found the following features very useful: DAG - Workload management and orchestration of tasks using.
Apache Airflow is a great orchestration and automation tool. Its connectivity with other systems is a great plus point. The interactive UI, the options for scheduling and the very fact that its compatibility with Python.
I like the UI rework, it's much easier.
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.
We have been quite satisfied with the stability of the solution.
The initial setup was straightforward and it does not take long to complete.
The product integrates well with other pipelines and solutions.
The reason we went with Airflow is its DAG presentation, that shows the relationships among everything. It's more of a configuration-driven workflow.
This is a simple tool to automate using Python.