Apache Airflow is an open source tool that can be used to programmatically author, schedule and monitor data pipelines using Python and SQL.
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Ansible
Score 9.0 out of 10
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The Red Hat Ansible Automation Platform (acquired by Red Hat in 2015) is a foundation for building and operating automation across an organization. The platform includes tools needed to implement enterprise-wide automation, and can automate resource provisioning, and IT environments and configuration of systems and devices. It can be used in a CI/CD process to provision the target environment and to then deploy the application on it.
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.
I think it's the best defacto orchestrator for automation because it's so easy to integrate in other tools to it (dynatrace, cyberark, terraform, etc). It is a lot for a new or smaller team to use so I wouldn't recommend it to a new team using Ansible, in that case using Ansible navigator is a better start to understanding playbooks/inventories before diving into the complexity of Red Hat Ansible Automation Platform. EDA also takes a lot of connectivity between Red Hat Ansible Automation Platform and target systems to get working which can be difficult in very locked down envs unless you have approval from many other teams like networking and security.
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.
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.
We are extremely happy with the use of AAP. It's better than expected, There is almost no limit when thinking of automation. The only problem is that the day to day is consuming a big part of our time. Patching and checking vulnerabilities are virtually killing us. But we can only improve with AAP.
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.
Everything has room for improvement, but Ansible is the best tool out there for what it does and what it can do. There are plenty of features and capabilities that can be added, but it's just a matter of time before it happens.
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.
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.
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.
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.
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
First, it keeps our entire server infrastructure aligned with our standards and reduces the time and effort needed to maintain our systems.
Automate routine IT tasks to save time, reduce errors, and ensure every server is configured and updated consistently.
Tasks that used to take our teams weeks to complete manually now run automatically and reliably, with full visibility, making our infrastructure management more effective and our compliance tracking much easier.
