Apache Airflow vs. Azure DevOps

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.

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Azure DevOps works well when you’ve got larger delivery efforts with multiple teams and a lot of moving parts, and you need one place to plan work, track it properly, and see how everything links together. It’s especially useful when delivery and development are closely tied and you want backlog items, code and releases connected rather than spread across tools. Where it’s less of a fit is for small teams or simple pieces of work, as it can feel like more setup and process than you really need, and non-technical users often struggle with the interface. It also isn’t great if you want instant, easy programme-level views or a very visual planning experience without putting time into configuration.

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• 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.

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• Utilize Git as a repository to share work between multiple users
• Ability to configure Pipelines to build containers to run virtual deployments and testing scripts.
• Split individual tasks and relate to master documents for quick navigation and ability to see overall picture of project.
• Track status of each task
• Integrate with Git to utilize branches, merging, approvals, history, etc.

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• 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.

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• I did mention it has good visibility in terms of linking, but sometimes items do get lost, so if there was a better way to manage that, that would be great.
• The wiki is not the prettiest thing to look at, so it could have refinements there.
• It could improve the search slightly better.

I don't think our organization will stray from using VSTS/TFS as we are now looking to upgrade to the 2012 version. Since our business is software development and we want to meet the requirements of CMMI to deliver consistent and high quality software, this SDLC management tool is here to stay. In addition, our company uses a lot of Microsoft products, such as Office 365, Asp.net, etc, and since VSTS/TFS has proved itself invaluable to our own processes and is within the Microsoft family of products, we will continue to use VSTS/TFS for a long, long time.

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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.

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It's a great help to get more information about new feature release and stay updated on what the dev team is working on. I like how easy it is to just login and read through the work items. Each work item has basic details: Title, Description, Assigned to, State, Area (what it belongs to), and iteration (when it’s worked on). See image above.They move through different states (New → Discovery → Ready for Prod → etc.).

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When we've had issues, both Microsoft support and the user community have been very responsive. DevOps has an active developer community and frankly, you can find most of your questions already asked and answered there. Microsoft also does a better job than most software vendors I've worked with creating detailed and frequently updated documentation.

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Was not part of the process.

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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.

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Microsoft Planner is used by project managers and IT service managers across our organization for task tracking and running their team meetings. Azure DevOps works better than Planner for software development teams but might possibly be too complex for non-software teams or more business-focused projects. We also use ServiceNow for IT service management and this tool provides better analysis and tracking of IT incidents, as Azure DevOps is more suited to development and project work for dev teams.

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• 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

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• We have saved a ton of time not calculating metrics by hand.
• We no longer spend time writing out cards during planning, it goes straight to the board.
• We no longer track separate documents to track overall department goals. We were able to create customized icons at the department level that lets us track each team's progress against our dept goals.

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info