Apache Airflow vs. CA Workload Automation

Airflow is well-suited for data engineering pipelines, creating scheduled workflows, and working with various data sources. You can implement almost any kind of DAG for any use case using the different operators or enforce your operator using the Python operator with ease. The MLOps feature of Airflow can be enhanced to match MLFlow-like features, making Airflow the go-to solution for all workloads, from data science to data engineering.

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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.

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• Apache Airflow is one of the best Orchestration platforms and a go-to scheduler for teams building a data platform or pipelines.
• Apache Airflow supports multiple operators, such as the Databricks, Spark, and Python operators. All of these provide us with functionality to implement any business logic.
• Apache Airflow is highly scalable, and we can run a large number of DAGs with ease. It provided HA and replication for workers. Maintaining airflow deployments is very easy, even for smaller teams, and we also get lots of metrics for observability.

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• 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.

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• UI/Dashboard can be updated to be customisable, and jobs summary in groups of errors/failures/success, instead of each job, so that a summary of errors can be used as a starting point for reviewing them.
• Navigation - It's a bit dated. Could do with more modern web navigation UX. i.e. sidebars navigation instead of browser back/forward.
• Again core functional reorg in terms of UX. Navigation can be improved for core functions as well, instead of discovery.

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

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For its capability to connect with multicloud environments. Access Control management is something that we don't get in all the schedulers and orchestrators. But although it provides so many flexibility and options to due to python , some level of knowledge of python is needed to be able to build workflows.

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Very happy!

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

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Multiple DAGs can be orchestrated simultaneously at varying times, and runs can be reproduced or replicated with relative ease. Overall, utilizing Apache Airflow is easier to use than other solutions now on the market. It is simple to integrate in Apache Airflow, and the workflow can be monitored and scheduling can be done quickly using Apache Airflow. We advocate using this tool for automating the data pipeline or process.

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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.

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• Impact Depends on number of workflows. If there are lot of workflows then it has a better usecase as the implementation is justified as it needs resources , dedicated VMs, Database that has a cost
• Donot use it if you have very less usecases

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• 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.

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info