Apache Airflow vs. Red Hat Ansible Automation Platform

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

I'm going to say it is best suited for configuration management. Like I said, patching even with security, things of that nature. Probably less suited is hardware management, but Red Hat IBM/IBM has Terraform for that. So it's a trade off.

Incentivized

• 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.

Incentivized

• Debugging is easy, as it tells you exactly within your job where the job failed, even when jumping around several playbooks.
• Ansible seems to integrate with everything, and the community is big enough that if you are unsure how to approach converting a process into a playbook, you can usually find something similar to what you are trying to do.
• Security in AAP seems to be pretty straightforward. Easy to organize and identify who has what permissions or can only see the content based on the organization they belong to.

Incentivized

• A local "dry run" or IDE plugin that can validate and simulate DAG execution without needing a full environment.
• Better feedback on DAG parse errors in the UI or CLI.
• Navigating large DAGs with hundreds of tasks can be slow and hard to understand visually.

Incentivized

• Ability to tell when a task has already been done
• yaml configuration can be annoying at times, perhaps a built in lint so yq isn't needed
• the become feature should be able to be set to true globally without using args

Incentivized

Even is if it's a great tool, we are looking to renew our licence for our production servers only. The product is very expensive to use, so we might look for a cheaper solution for our non-production servers. One of the solution we are looking, is AWX, free, and similar to AAP. This is be perfect for our non-production servers.

Incentivized

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.

Incentivized

Overall it's good but the new architecture can be complex. Improvements can be made in the Config as Code capabilities for managing Red Hat Ansible Automation Platform. Sometimes it can be difficult for those unfamiliar to understand the relationship between Projects/Credentials/Job Templates, etc.

Incentivized

Great in almost every way compared to any other configuration management software. The only thing I wish for is python3 support. Other than that, YAML is much improved compared to the Ruby of Chef. The agentless nature is incredibly convenient for managing systems quickly, and if a member of your term has no terminal experience whatsoever they can still use the UI.

Incentivized

There is a lot of good documentation that Ansible and Red Hat provide which should help get someone started with making Ansible useful. But once you get to more complicated scenarios, you will benefit from learning from others. I have not used Red Hat support for work with Ansible, but many of the online resources are helpful.

Incentivized

I spoke on this topic today!

Incentivized

Apache Airflow is suited for a much wider set of use cases compared to Databricks. You can run it anywhere, and there is also no vendor lock-in. With Airflow, we can utilize almost any compute engine. Same thing we want to do with Databricks. There might be some level of difficulty based on the support.

Incentivized

As I said earlier, Red Hat Ansible remains a top choice because it is a perfect combination of multiple capabilities. Terraform is good in IAC but not in config automation. Puppet is well-suited for developers, but not for system administrators and infrastructure integrators. OpenShift and Kubernetes are generic automators only.

Incentivized

• Most of the ETL processes were automated, cutting down on human labor.
• Apache Airflow's user interface (UI) was very informative and straightforward.
• Since ETL processes were providing data via airflow, we were able to gain a deeper comprehension of the data at hand.

Incentivized

• We are still early in our implementation and don't have much yet - but I can say that it has already improved the time it takes to deploy a new virtual server for us, as well as making them more consistent.
• In working through what jobs are required, it has really improved the communication between our different teams

Incentivized