Azure DevOps Server vs. Posit

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

In my humble opinion, if you are working on something related to Statistics, RStudio is your go-to tool. But if you are looking for something in Machine Learning, look out for Python. The beauty is that there are packages now by which you can write Python/SQL in R. Cross-platform functionality like such makes RStudio way ahead of its competition. A couple of chinks in RStudio armor are very small and can be considered as nagging just for the sake of argument. Other than completely based on programming language, I couldn't find significant drawbacks to using RStudio. It is one of the best free software available in the market at present.

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

• The support is incredibly professional and helpful, and they often go out of their way to help me when something doesn't work.
• The one-click publishing from RStudio Connect is absolutely amazing, and I really like the way that it deploys your exact package versions, because otherwise, you can get in a terrible mess.
• Python doesn't feel quite as native as R at the moment but I have definitely deployed stuff in R and Python that works beautifully which is really nice indeed.

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

• Python integration is newer and still can be rough, especially with when using virtual environments.
• RStudio Connect pricing feels very department focused, not quite an enterprise perspective.
• Some of the RStudio packages don't follow conventional development guidelines (API breaking changes with minor version numbers) which can make supporting larger projects over longer timeframes difficult.

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

There is no viable alternative right now. The toolset is good and the functionality is increasing with every release. It is backed by regular releases and ongoing development by the RStudio team. There is good engagement with RStudio directly when support is required. Also there's a strong and growing community of developers who provide additional support and sample code.

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

For someone who learns how to use the software and picks up on the "language" of R, it's very easy to use. For beginners, it can be hard and might require a course, as well as the appropriate statistical training to understand what packages to use and when

Incentivized

RStudio is very available and cheap to use. It needs to be updated every once in a while, but the updates tend to be quick and they do not hinder my ability to make progress. I have not experienced any RStudio outages, and I have used the application quite a bit for a variety of statistical analyses

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

Since R is trendy among statisticians, you can find lots of help from the data science/ stats communities. If you need help with anything related to RStudio or R, google it or search on StackOverflow, you might easily find the solution that you are looking for.

Incentivized

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

Incentivized

We did it at the individual level: anyone willing to code in R can use it. No real deployment involved.

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

RStudio was provided as the most customizable. It was also strictly the most feature-rich as far as enabling our organization to script, run, and make use of R open-source packages in our data analysis workstreams. It also provided some support for python, which was useful when we had R heavy code with some python threaded in. Overall we picked Rstudio for the features it provided for our data analysis needs and the ability to interface with our existing resources.

Incentivized

RStudio is very scalable as a product. The issue I have is that it doesn't necessarily fit in nicely with the mainly Microsoft environment that everybody else is using. Having RStudio for us means dedicated servers and recruiting staff who know how to manage the environment. This isn't a fault of the product at all, it's just part of the data science landscape that we all have to put up with. Having said that RStudio is absolutely great for running on low spec servers and there are loads of options to handle concurrency, memory use, etc.

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

• Using it for data science in a very big and old company, the most positive impact, from my point of view, has been the ability of spreading data culture across the group. Shortening the path from data to value.
• Still it's hard to quantify economic benefits, we are struggling and it's a great point of attention, since splitting out the contribution of the single aspects of a project (and getting the RStudio pie) is complicated.
• What is sure is that, in the long run, RStudio is boosting productivity and making the process in which is embedded more efficient (cost reduction).

Incentivized