Apache Airflow vs. Azure 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.

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

• 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

• 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

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

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

• 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

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

Incentivized