Apache Airflow vs. Lumen Cloud Application Manager

For a quick job scanning of status and deep-diving into job issues, details, and flows, AirFlow does a good job. No fuss, no muss. The low learning curve as the UI is very straightforward, and navigating it will be familiar after spending some time using it. Our requirements are pretty simple. Job scheduler, workflows, and monitoring. The jobs we run are >100, but still is a lot to review and troubleshoot when jobs don't run. So when managing large jobs, AirFlow dated UI can be a bit of a drawback.

Incentivized

It was very good to use in small scale projects. Considering the high end projects with many instances and multi-platform architectures, it is better to test before the application is deployed. I think few of the questions can be general - who are the system users and what size is the application focussing on? How much resources are required? Will the application require any additional services?

Incentivized

• In charge of the ETL processes.
• As there is no incoming or outgoing data, we may handle the scheduling of tasks as code and avoid the requirement for monitoring.

Incentivized

• Quick deployment of pre-built virtual machines
• Some of the virtual machines also are readily available with a pack of softwares
• Good Stability. Using it as a student, I have never experienced any downtime issues with the projects deployed on AppFog.

Incentivized

• they should bring in some time based scheduling too not only event based
• they do not store the metadata due to which we are not able to analyze the workflows
• they only support python as of now for scripted pipeline writing

Incentivized

• Though it is good and easy for developers, it lacks operational activities and monitoring tools.
• It is easy to deploy WAR on AppFog with its console but sometimes it can lack on the performance and feasibility.

Incentivized

There are a number of reasons to choose Apache Airflow over other similar platforms- Integrations—ready-to-use operators allow you to integrate Airflow with cloud platforms (Google, AWS, Azure, etc) Apache Airflow helps with backups and other DevOps tasks, such as submitting a Spark job and storing the resulting data on a Hadoop cluster It has machine learning model training, such as triggering a Sage maker job.

Incentivized

Primarily because it used to have a good free tier earlier, which it does not anymore. It's simple, and things are available to use. Compared to it's competitors, it does has less features, but that kind of acts in its favor. That adds to the simplicity, and ease of use for a new user.

Incentivized

• A lot of helpful features out-of-the-box, such as the DAG visualizations and task trees
• Allowed us to implement complex data pipelines easily and at a relatively low cost

Incentivized

• Our project was deployed with good efficiency and easily accessible.
• The platform was much recommeded across the groups and peers.

Incentivized