Apache Airflow vs. CA Workload Automation

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

CA Workload Automation is well suited for firms having to manage and monitor multiple applications operating in multiple environments. and having SLA requirements. CA Workload Automation is less appropriate and not cost-effective for non-critical applications and/or jobs. CA Workload Automation is ideal for companies that require reliable and stable Production support for their business critical jobs to finish on time.

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

• All batch jobs can be monitored from one dashboard.
• Provides detailed error logs.
• We have developed packages to invoke OIC integrations through Autosys.
• Notifications is a good feature when job fails or runs beyond cutoff time.

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 of front-end websites.
• More utilization insights if possible.
• Customer service via chat in the future if possible.

Incentivized

Very happy!

Incentivized

It would be great if customer chat experience is also readily available.

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

We selected CA Workload Automation for the ease of integration and less time to setup. It has less overhead to manage the application and is a very robust application.

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

• CA Workload Automation has improved SLA by 50%.
• CA Workload Automation has reduced job failures by 60%.
• CA Workload Automation has improved staff productivity and problem resolution.
• CA Workload Automation has improved application availability.

Incentivized