What users are saying about
101 Ratings
12 Ratings
101 Ratings
<a href='https://www.trustradius.com/static/about-trustradius-scoring' target='_blank' rel='nofollow'>trScore algorithm: Learn more.</a>
Score 8.6 out of 101
12 Ratings
<a href='https://www.trustradius.com/static/about-trustradius-scoring' target='_blank' rel='nofollow'>trScore algorithm: Learn more.</a>
Score 7.6 out of 101

Add comparison

Likelihood to Recommend

Apache Spark

If you are running a distributed environment and are running applications that make use of batch processing, analytics, streaming, machine learning, or graphing then I cannot recommend Spark enough. It is easy to get going, simple to learn (relative to similar technologies), and can be used in a variety of use cases. All while giving you great performance.
No photo available

Microsoft R

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.
Shailesh Deshpande profile photo

Pros

  • Ease of use, the Spark API allows for minimal boilerplate and can be written in a variety of languages including Python, Scala, and Java.
  • Performance, for most applications we have found that jobs are more performant running via Spark than other distributed processing technologies like Map-Reduce, Hive, and Pig.
  • Flexibility, the frameworks comes with support for streaming, batch processing, sql queries, machine learning, etc. It can be used in a variety of applications without needing to integrate a lot of other distributed processing technologies.
No photo available
  • 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
Shailesh Deshpande profile photo

Cons

  • Resource heavy, jobs, in general, can be very memory intensive and you will want the nodes in your cluster to reflect that.
  • Debugging, it has gotten better with every release but sometimes it can be difficult to debug an error due to ambiguous or misleading exceptions and stack traces.
No photo available
  • 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.
Shailesh Deshpande profile photo

Likelihood to Renew

No score
No answers yet
No answers on this topic
Microsoft R7.0
Based on 1 answer
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.
Shailesh Deshpande profile photo

Alternatives Considered

I prefer Apache Spark compared to Hadoop, since in my experience Spark has more usability and comes equipped with simple APIs for Scala, Python, Java and Spark SQL, as well as provides feedback in REPL format on the commands. At the same time, Apache Spark seems to have the best performance in the processing of large data that works in memory and, therefore, more processes can be downloaded on Spark than on Hadoop, despite the fact that Hadoop is also a very useful tool.
Carla Borges profile photo
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.
Shailesh Deshpande profile photo

Return on Investment

  • We were able to make batch job faster by 20 times as compared to MapReduce
  • With the language support like Scala, Java, and Python, easily manageable
No photo available
  • 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.
Shailesh Deshpande profile photo

Pricing Details

Apache Spark

General
Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No
Additional Pricing Details

Microsoft R

General
Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No
Additional Pricing Details