Microsoft R Open and Revolution R Enterprise are big data R distribution for servers, Hadoop clusters, and data warehouses. Microsoft acquired original developer Revolution Analytics in 2016.
Microsoft R is available in two editions: Microsoft R Open (formerly Revolution R Open) and Revolution R Enterprise.
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Posit
Score 9.9 out of 10
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Posit, formerly RStudio, is a modular data science platform, combining open source and commercial products.
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Pricing
Microsoft R Open / Revolution R Enterprise
Posit
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Microsoft R Open / Revolution R Enterprise
Posit
Free Trial
No
Yes
Free/Freemium Version
No
Yes
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
Optional
Additional Details
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More Pricing Information
Community Pulse
Microsoft R Open / Revolution R Enterprise
Posit
Features
Microsoft R Open / Revolution R Enterprise
Posit
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Microsoft R Open / Revolution R Enterprise
5.3
3 Ratings
45% below category average
Posit
8.8
27 Ratings
4% above category average
Connect to Multiple Data Sources
6.13 Ratings
7.926 Ratings
Extend Existing Data Sources
6.03 Ratings
9.327 Ratings
Automatic Data Format Detection
6.03 Ratings
9.326 Ratings
MDM Integration
3.01 Ratings
00 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Microsoft R Open / Revolution R Enterprise
7.0
3 Ratings
18% below category average
Posit
9.1
27 Ratings
8% above category average
Visualization
7.03 Ratings
8.227 Ratings
Interactive Data Analysis
7.03 Ratings
9.924 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Microsoft R Open / Revolution R Enterprise
4.8
3 Ratings
52% below category average
Posit
9.7
26 Ratings
17% above category average
Interactive Data Cleaning and Enrichment
5.13 Ratings
9.824 Ratings
Data Transformations
5.03 Ratings
9.726 Ratings
Data Encryption
3.01 Ratings
00 Ratings
Built-in Processors
6.03 Ratings
00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Microsoft R Open / Revolution R Enterprise
6.0
3 Ratings
34% below category average
Posit
9.8
22 Ratings
15% above category average
Multiple Model Development Languages and Tools
5.03 Ratings
9.722 Ratings
Automated Machine Learning
5.02 Ratings
00 Ratings
Single platform for multiple model development
8.03 Ratings
9.722 Ratings
Self-Service Model Delivery
6.03 Ratings
10.019 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
If you are a MS shop specifically, or have more generic data requirement needs from Microsoft sourced data this will work well. If you have a lot of disparate data across a number of unique platforms/cloud systems/3rd party hosted data warehouses then this product will have issues or a lack of documentation on the net. Performance-wise this product is equal to other R platforms out there.
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.
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.
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.
In general, Revolution Analytics brings a lot of value to the organization. The renewal decision would be based on return on investment in terms of quantified actionable insights that are getting generated against the cost of the product. Additionally, market brand of the tool and reputation risk in terms of possible acquisition and its impact to overall organizational analytic strategy would be considered as well.
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.
It is good, easy to use, improvements are being made to the product and more info being shared in the community. It just needs some more time to become more integrated to other platforms and tools/data out there.
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
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
Generally support comes through the forums and user generated channels which are helpful, easy to access, quickly turned around and provided by knowledgeable users. However the support channels are not employees and the channels are often used as a way to learn quick difficult elements of R. Better design, users interface and tutorial options would alleviate the need for this sort of interaction.
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.
The two are different products for different purposes. But for someone who has little or no experience in R programming, Power BI would be better for starting with. Having said that, Microsoft R is built on R, thus allowing for customization of complex calculations not typically available otherwise.
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.
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.
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).