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Microsoft R Open / Revolution R Enterprise

Microsoft R Open / Revolution R Enterprise

Overview

What is Microsoft R Open / Revolution R Enterprise?

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…

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Pricing

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What is Microsoft R Open / Revolution R Enterprise?

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…

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Product Details

What is Microsoft R Open / Revolution R Enterprise?

Microsoft R Open / Revolution R Enterprise Technical Details

Operating SystemsUnspecified
Mobile ApplicationNo

Frequently Asked Questions

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.

Reviewers rate Single platform for multiple model development highest, with a score of 8.

The most common users of Microsoft R Open / Revolution R Enterprise are from Enterprises (1,001+ employees).
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Comparisons

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Reviews and Ratings

(16)

Attribute Ratings

Reviews

(1-5 of 5)
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Score 6 out of 10
Vetted Review
Verified User
Incentivized
We implemented Microsoft R along side Windows Subsystem for Linux with Airflow to do ETL data loading last year through until late this year. We had some success with both products but due to limitations to Airflow and R not being completely fully functioning versus open source R suites we have cancelled both tools not long ago. I can say during its time it was easy to set up, R was easier to use to send processing jobs through than other solutions before and after. It just did not meet our needs once we got ETL loading to a more robust point.
  • Easy to set up on a windows system and integrate to network drives for data integration.
  • Allows easy authenticated user access to the tool to send jobs (one less user/pw on a domain).
  • Easy to integrate to other windows applications that are installed beside the software on the same system and on other windows remote systems.
  • Relatively low issues in the configuration phase to enable data processing.
  • It is not fully featured vs linux open source versions out there.
  • It is not as easy to find (or as much) online documentation when having issues with the software.
  • Use of this tool alongside WSL applications is still too early. Once WSL gets more general use then this will be a better tool.
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.
Platform Connectivity (3)
60%
6.0
Connect to Multiple Data Sources
60%
6.0
Extend Existing Data Sources
60%
6.0
Automatic Data Format Detection
60%
6.0
Data Exploration (2)
70%
7.0
Visualization
70%
7.0
Interactive Data Analysis
70%
7.0
Data Preparation (3)
53.33333333333333%
5.3
Interactive Data Cleaning and Enrichment
50%
5.0
Data Transformations
50%
5.0
Built-in Processors
60%
6.0
Platform Data Modeling (4)
60%
6.0
Multiple Model Development Languages and Tools
50%
5.0
Automated Machine Learning
50%
5.0
Single platform for multiple model development
80%
8.0
Self-Service Model Delivery
60%
6.0
Model Deployment (2)
65%
6.5
Flexible Model Publishing Options
60%
6.0
Security, Governance, and Cost Controls
70%
7.0
  • We took the learnings of this product and used it towards our replaced platform so no real lost investment. Just time was lost during the project when trying to integrate and use this along side our other ETL tools.
  • This did allow us to identify other improvements (not realized before the project) to data analysis during the way that we have continued to leverage post project migration.
R is decent for our needs but in the end didn't quite solve all of our needs so moved on. It is a good tool so far. its been a couple months since we last touched it so with changes continuing and more wide spread use and more info being published this tool will improve. Depending upon your needs this can be very easy for you to setup, use, and maintain when compared to other tools out there. My suggestion is to ensure you fully understand your use cases first with data sources identified to ensure this tool can meet your needs.
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.
MS support is good but sometimes they are slow at finding resolutions to either new issues or understanding the customers needs in feature issues.
Score 5 out of 10
Vetted Review
Verified User
Incentivized
My organisation has 40+ services ranging from 00s to millions of users, therefore iterations, changes and forecasting must be done in a controlled, rigorous and statistically appropriate manor as well as using tools that are efficient and provide analytics the ability to interrogate data en masse. R provides this.
  • Comprehensive and highly populated repositories allowing for a lot of statistical work to be done quickly.
  • Forecasting tools are solid and have provided quick and accurate insight.
  • Very steep learning curve... for such a quick and useful tool the learning curve is unacceptable.
  • Very dangerous in the wrong hands. Because most add-ons are pre-written, you have to trust the community that malicious script is not used.
The main use of R for us is in structural forecasting and joining data together.

Using R to forecast gives our analysts the flexibility to interrogate a lot of data to give better modelling accuracy. This includes plugging in per second and minute data over multiple years for services with 100s of millions of interactions per year. Our seasonality means we can often shoot up from 00's to millions of users in a matter of hours, so knowing this will help allocate resource, both server, helplines and support.
Platform Connectivity (4)
67.5%
6.8
Connect to Multiple Data Sources
90%
9.0
Extend Existing Data Sources
70%
7.0
Automatic Data Format Detection
80%
8.0
MDM Integration
30%
3.0
Data Exploration (2)
60%
6.0
Visualization
70%
7.0
Interactive Data Analysis
50%
5.0
Data Preparation (4)
57.5%
5.8
Interactive Data Cleaning and Enrichment
80%
8.0
Data Transformations
60%
6.0
Data Encryption
30%
3.0
Built-in Processors
60%
6.0
Platform Data Modeling (4)
60%
6.0
Multiple Model Development Languages and Tools
60%
6.0
Automated Machine Learning
50%
5.0
Single platform for multiple model development
70%
7.0
Self-Service Model Delivery
60%
6.0
Model Deployment (2)
55%
5.5
Flexible Model Publishing Options
70%
7.0
Security, Governance, and Cost Controls
40%
4.0
  • Better forecasting for resource allocation has saved our organisation hundreds of thousands in conjunction with other strategies.
  • Better visualisation options has allowed smoother internal marketing and internal comms strategies when preparing teams for seasonality.
eViews is used as an alternative statistical modelling package as it is more user friendly, less scripted and has many more quick and easy data evaluation elements to it, however does not contain the flexibility and breadth of scripting and output options as widely supported as R does. eViews is mainly an academic tool and while it has a lot of flexibility to use and support is not as comprehensive as the user support channels simply aren't there.
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.
Score 8 out of 10
Vetted Review
Verified User
Incentivized
Microsoft R is used by specific users when parallel processing is desired. Otherwise, most are fine with Base R.
  • Parallel processing
  • Integration with R
  • Open-source
  • R itself is a programming language, so there is some learning curve
  • Sometimes interferes with upgrading R packages
Well suited if parallel processing is desired, eg. giant datasets or complex calculations. Otherwise, R studio might be sufficient. In either case, some level of programming in R will be required.
Platform Connectivity (4)
60%
6.0
Connect to Multiple Data Sources
80%
8.0
Extend Existing Data Sources
80%
8.0
Automatic Data Format Detection
80%
8.0
MDM Integration
N/A
N/A
Data Exploration (2)
75%
7.5
Visualization
80%
8.0
Interactive Data Analysis
70%
7.0
Data Preparation (4)
60%
6.0
Interactive Data Cleaning and Enrichment
80%
8.0
Data Transformations
80%
8.0
Data Encryption
N/A
N/A
Built-in Processors
80%
8.0
Platform Data Modeling (4)
62.5%
6.3
Multiple Model Development Languages and Tools
90%
9.0
Automated Machine Learning
N/A
N/A
Single platform for multiple model development
80%
8.0
Self-Service Model Delivery
80%
8.0
Model Deployment (2)
N/A
N/A
Flexible Model Publishing Options
N/A
N/A
Security, Governance, and Cost Controls
N/A
N/A
  • Increased productivity (Ability for parallel processing freed up time to do other tasks)
  • Easy installation meant no need for dedicated IT support
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.
Score 7 out of 10
Vetted Review
Verified User
Incentivized
Microsoft R is typically used for statistics and data analysis needs for marketing data and developing machine learning algorithms. It helps solve optimization and developing predictive models. R is currently being used by one person in the department considering the size of the company, but looking to add more users of R sooner or later.
  • It's free
  • It's open source
  • Does the same statistical and data analysis as other programs
  • Very high learning curve
It's well suited for developing machine learning algorithms and statistical/data analysis. However, analysis in R does take some time so if you're churning a high number of projects, you might want to consider something else.
  • Helped save company money versus buying other stat software
R requires knowledge of programming and can be a high learning curve versus if you're using a user-friendly SPSS or JMP.
Shailesh Deshpande | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
I have used Revolution Analytics Rev-R enterprise 7.0 for data analytics project. I was also engaged in beta-testing release D as well. Rev-R actually solves a Big Data gap by allowing data scientists to load big data in Hadoop HDFS and run complex algorithms such as Random Forest or decision trees by running the algorithms in a distributed way on the cluster. That helps to draw insights from big data sets without having to script complex programs in say Java or Python.
  • It allows distributed algorithm runs on Hadoop HDFS cluster
  • It allows using different file formats such as SAS7BAT files or complex files in tab or comma delimited making data munging easier
  • It provides scalable solutions by allowing users to re-use R scripts and distributing the computing over nodes through RHadoop
  • When I reviewed the product - release D, at that time, "decision forest algorithm" was not available.
  • The tool needs to be more integrated with other data infrastructure tools such as Teradata, Informatica etc. as well as may be with new Hadoop distribution platforms such as Cloudera or Hortonworks so the users don't have to install the tool from scratch
  • I would also like to see improved capability around GUI and integration with other ecosystem. As the Big Data ecosystem would evolve in next 2-3 years, I would like to see Rev-R becoming more compatible with start-ups as well.
Revolution Analytics is a very compelling product for Big Data Analytics. It allows distributed computing over multiple hadoop nodes thus allowing HDFS to do its role cleanly i.e. cheap massive storage and it does good job of running algorithms using R or similar programming language on Hadoop. It would be definitely advantage for the organization who uses either R or SAS as their statistical model development tool as Rev-R support both the platforms. Overall, very positive experience with Rev-R.
  • Faster time-to-market on analytics and insights
  • Reduction on Level of Effort in terms of running complex algorithmm thus increased job satisfaction
  • Improved job empowerment and skills/competency re-use.
My understanding is Revolution Analytics Enterprise version is not cheap. Thus alternatives for the software could be Hadoop/HDFS level programming using Python and Mahout to achieve same distributed computing. Additionally, Cloudera is coming up with new data science tool called Oryx, which could be competitor to Rev-R. But, the tool selection at every organization would depend on the strategy and cost that is budgeted.
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.
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