Apache Airflow vs. CA Scheduler Job Management

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

Job scheduling software for the IBM Mainframe is quite mature and most IBM Mainframe sites would already have one installed. It would appear to be very little reason to change from one scheduling package to another. If you were looking for a new scheduling package, CA Technologies is more likely to steer you towards their other product

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

• Good reporting on completed schedules and jobs including highest return code of jobs.
• ISPF interface can cope with non-standard screen sizes.
• Easy to use ISPF interface

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

• Monitoring interface could make better use of screen colours

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

All quite similar and as CA Scheduler had the smallest foot print across the agencies we support it is being decommmissioned

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

• Being decommissioned due to software rationalisation.

Incentivized