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Score 8.3 out of 101
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Score 7.6 out of 101

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Likelihood to Recommend

Amazon EMR

Amazon Elastic MapReduce is useful in cases where two conditions are met. First, that you are planning on using multiple big data tools simultaneously to analyze big data sets. And second, that you need a tool that simplifies managing big data tools. If these two conditions are met, MapReduce does a great job. The user interface is simple. The program eliminates some programming requirements. The software also makes setting up big data analyses much easier. With these benefits acknowledged, MapReduce is not a good tool for "small" data analyses, given that there are other tools that do the job quicker and much more professional output. If you're on the fence, try out MapReduce with competing "small" data tools and see if you really need big data software.
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Data Science Workbench

  • If you already have a Cloudera partnership and a cluster, having this is a no brainer.
  • It integrates well with your existing ecosystem and it immediately starts working on projects, accessing full datasets and share analysis and results.
  • With the inclusion of Kubernetes, CPU and memory across worker nodes can be managed effectively.
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Pros

  • Ease of use and ease to setup
  • Autoscaling functionality
  • Integrated into the AWS environment
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  • One single IDE (browser based application) that makes Scala, R, Python integrated under one tool
  • For larger organizations/teams, it lets you be self reliant
  • As it sits on your cluster, it has very easy access of all the data on the HDFS
  • Linking with Github is a very good way to keep the code versions intact
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Cons

  • Cost overhead is a bit high
  • Limited versions of frameworks that can be used
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  • Not as great as RStudio; lacks some features when compared with it
  • It is quite simple still (because its very early in its initiative), and companies may want to wait until they see a more developed product
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Alternatives Considered

The alternatives to EMR are mainly hadoop distributions owned by the 3 companies above. I have not used the other distributions so it is difficult to comment, but the general tradeoff is, at the cost of a longer setup time and more infra management, you get more flexible versioning and potentially faster access to newer versions of some frameworks such as Spark.
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Both the tools have similar features and have made it pretty easy to install/deploy/use. Depending on your existing platform (Cloudera vs. Azure) you need to pick the Workbench. Another observation is that Cloudera has better support where you can get feedback on your questions pretty fast (unlike MS). As its a new product, I expect MS to be more efficient in handling customers questions.
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Return on Investment

  • It was easy to set up initial versions of Spark on this
  • Still used as our compute platform as its easy to manage
  • Certain times we forgot to shut down clusters and were overcharged
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  • As the tool itself can access all the HDFS, Spark data easily, the wait time between teams has reduced
  • Installation was a breeze, and ramp up time was fairly easy
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Pricing Details

Amazon EMR

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

Data Science Workbench

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