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Score 8.3 out of 101
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Score 8 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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MapR

MapR is more well-suited for people who know what they are doing. I consider MapR the Hadoop distribution professionals use.
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Pros

  • Distributed computing
  • Fault tolerant
  • Uptime
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  • MapR is fast. We were able to beat the Terasort in record in 2012 on 360 nodes during the initial deployment of a cluster that is now 4000 nodes.
  • MapR is reliable. We rarely if ever have problems deploying MapR. It's the kind of software that "just works."
  • MapR scales. We have a client using MapR in all their big data clusters, ranging from 50 to 630 machines. Test, development, and production all deploy MapR.
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Cons

  • Cost overhead is a bit high
  • Limited versions of frameworks that can be used
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  • I think MapR's main problem is name recognition. Hortonworks and Cloudera both are big names in the industry, but their deployment mechanisms are a little more difficult to use, especially when trying to fully automate it's deployment.
  • Documentation could always be better. But really, if that's your main weakness, it's everybody's weakness.
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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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Hortonworks and Cloudera are both sort of hacky. We have to do a lot of extra steps to automate those two. MapR has far fewer issues and doesn't force you into a once size fits all deployment scenario. There are multiple ways to deploy and some are more amenable to automation, MapR just has that in spades
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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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  • Increased employee efficiency for sure. Our clients have various levels of expertise in their deployment and user teams, and we never receive complaints about MapR.
  • MapR is used by one of our financial services clients who uses it for fraud detection and user pattern analysis. They are able to turn around data much faster than they previously had with in-house applications
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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

MapR

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