Apache Airflow vs. Azure DevOps Server

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

Azure DevOps is good to use if you are all-in on the Microsoft Azure stack. It's fully integrated across Azure so it is a point-and-click for most of what you will need to achieve. If you are new to Azure make sure you get some outside experience to help you otherwise it is very easy to overcomplicate things and go down the wrong track, or for you to manually create things that come out of the box.

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

• Reporting Integration- Azure boards provides Kanban and other dashboard, their templates for easy management of project.
• Project Pipeline- easy integration and development of CI/CD pipelines, helped in testing, releasing project artifacts.
• Version Control- Integration with Git and code IDE made it easy to share, review our code, fix bugs and do testing.

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

• Can add more build templates for specific technology requirements
• Can have more features in dashboards which can help dev teams stream line their tasks and priorities
• Can have raise alarm feature in case of any sort of failure in devops pipeline execution

Incentivized

Because we are a Microsoft Gold Partner we utilize most of their software and we have so much invested in Team Foundation Server now it would take a catastrophic amount of time and resources to switch to a different product.

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

For standard users the interface is friendly. but if you are a manager some tools are a little confusing to use, like the query system that you always need to create from scratch. Templates should be more helpful for queries and for standard procedures that you need to duplicate PBIs over time. The search history of Work Items is a little painful to use.

Incentivized

I have not had to use the support for Azure DevOps Server. There have never been any issues where I was not able to figure it out or quickly resolve. Our Scrum Master has used support before though, and the service has always been prompt and clear with a customer-focus

Incentivized

Do research beforehand and, if possible, do a trial run before implementing into production environment.

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

In my opinion, DevOps covers the development process end to end way better than Jira or GitHub. Both competitors are nice in their specific fields but DevOps provides a more comprehensive package in my opinion. It is still crazy to see that the whole suite can be used for free. The productivity increase we realized with DevOps is worth real money!

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

• It has streamlined the pipeline and project management for our agile effort.
• It has helped our agile team get organized since that is a new methodology being leveraged within the Enterprise.
• The calendar has improved visibility into different OOOs across the project team since we all come from different departments across the larger organization.

Incentivized