Apache Airflow vs. Azure 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

We have found Azure Automation to be well-suited for tasks that must be performed on a scheduled basis. No matter how simple or tedious tasks are to perform manually, if they run on a scheduled basis, it makes sense to have them automated. If the tasks at hand are only performed once or infrequently, then it may be best to continue performing them manually.

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

• Scheduled automation of Microsoft 365 and Azure tasks
• Scheduled automation of scripting (e.g. Powershell)
• Perform periodic maintenance

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

• An automation template library would be nice
• Clear estimation of costs - this goes for the entire Azure Automation platform
• It can be daunting at first due to all the settings and menu levels, but it is not difficult once you get started.

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

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

We did not evaluate any other automation products since we are already using Microsoft 365 and the Azure platform. Additionally, Azure Automation allows for a reasonable number of tasks to be performed each month at no cost, which is especially nice when testing the platform. We found Azure Automate's integration of Powershell invaluable when working directly with Microsoft 365, Sharepoint, Azure AD, and other systems via API.

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

• Ensures tasks are not forgotten
• Saves times - routine tasks no longer have to be performed manually
• Complicated tasks are no longer an issue

Incentivized