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

Azure is specifically suited for businesses that work with customers that need to work with Microsoft software products. Any instances of Microsoft products or suites that require an environment test to set up would definitely benefit from this tool. As an IT support, it is relatively easy to support and use. Where this tool is not useful if a customer has no need for it.

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

• Managing the users
• Having multiple environments
• Creating multiple groups

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

• The user interface, in my opinion, might need further clarification.
• Any situation where a user's password has to be reset would benefit from this feature.
• Any accounts that were accidentally established more than once may be transferred over quickly and easily.

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

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

• 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

• After initial setup, we now have significantly less time spent processing data.
• The automation of data formatting and display after processing is exciting because it frees us to focus on the data itself.
• Since using Batch, we have significantly decreased the number of items we previously utilized.

Incentivized