Apache Airflow vs. Red Hat Ansible Automation Platform

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.

Incentivized

For automating the configuration of a multi-node, multi-domain (Storage, VM, Container) cluster, Ansible is still the best choice; however, it is not an easy task to achieve. Creating the infrastructure layer, i.e., creating network nodes, VMs, and K8s clusters, still can't be achieved via Ansible. Additionally, error handling remains complex to resolve.

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

• 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.

Incentivized

• I can't think of any right now because I've heard about the Lightspeed and I'm really excited about that. Ansible has been really solid for us. We haven't had any issues. Maybe the upgrade process, but other than that, as coming from a user, it's awesome.
• Give out Lightspeed for free.

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

It's overall pretty easy to use foe all the applications I've mentioned before: configuring hosts, installing packages through tools like apt, applying yaml, making changes across wide groups of hosts, etc. Its not a 10 because of the inconveinience of the yaml setup, and the time to write is not worth it for something applied one time to only a few hosts

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

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.

Incentivized

AAP compares favorably with Terraform and Power Automate. I don't have much experience with Terraform, but I find AAP and Ansible easier to use as well as having more capabilities. Power Platform is also an excellent automation tool that is user friendly but I feel that Ansible has more compatibility with a variety of technologies.

Incentivized

• 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

Incentivized

• POSITIVE: currently used by the IT department and some others, but we want others to use it.
• NEGATIVE: We need less technical output for the non-technical. It should be controllable or a setting within playbooks. We also need more graphical responses (non-technical).
• POSITIVE: Always being updated and expanded (CaC, EDA, Policy as Code, execution environments, AI, etc..)

Incentivized