Apache Airflow vs. Azure Batch

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

To better serve their consumers, businesses that often interact with those clients who rely on Microsoft's software products may consider migrating to Azure. This program would be useful in any installation of a Microsoft product or suite that necessitates a test of the target environment. It is simple to maintain and implement, making it an ideal IT backbone. If a client doesn't have any use for this particular instrument, it's not going to be of any benefit to them.

Incentivized

• In charge of the ETL processes.
• As there is no incoming or outgoing data, we may handle the scheduling of tasks as code and avoid the requirement for monitoring.

Incentivized

• Ease of scripting
• Scheduling
• Combination of Data Sets

Incentivized

• they should bring in some time based scheduling too not only event based
• they do not store the metadata due to which we are not able to analyze the workflows
• they only support python as of now for scripted pipeline writing

Incentivized

• The UI could be a little more clearer in my opinion.
• Password resets would be nice to have in any instance (not just the default area).
• Easier to migrate repeated account that may have been created.

Incentivized

There are a number of reasons to choose Apache Airflow over other similar platforms- Integrations—ready-to-use operators allow you to integrate Airflow with cloud platforms (Google, AWS, Azure, etc) Apache Airflow helps with backups and other DevOps tasks, such as submitting a Spark job and storing the resulting data on a Hadoop cluster It has machine learning model training, such as triggering a Sage maker job.

Incentivized

Both are excellent resources that successfully deliver the promised benefits. Two rival businesses, each with its own distinct culture and set of goals. As far as IT assistance goes, I find Azure's user interface to be slightly more intuitive. Both resources are valuable and have their advantages and disadvantages. Both are crucial if you run a fast-paced business with a large consumer base.

Incentivized

• A lot of helpful features out-of-the-box, such as the DAG visualizations and task trees
• Allowed us to implement complex data pipelines easily and at a relatively low cost

Incentivized

• After setting things up we have reduced time in running all of the processing
• It is promising in allowing us to concentrate on the data rather than the formatting and presentation that can all be automated after it has been processed
• We have reduced the number of products we used before adopting Batch

Incentivized