What users are saying about

Amazon EMR

24 Ratings

MapR

14 Ratings

Amazon EMR

24 Ratings
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Score 8.3 out of 101

MapR

14 Ratings
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Score 7.8 out of 101

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Likelihood to Recommend

Amazon EMR

If you don't have big data ..i.e petabytes of data with terabytes of data generating every day, then don't use Hadoop. Relational databases are enough for terabytes of data. Hadoop is not well suited for transactional systems or data.
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MapR

If you need Hadoop and just need raw speed for I/O and have a Hadoop savvy group of engineers who don't need/like web UIs, then MapR is a great fit for you. If you are new to Hadoop or have DevOps folks that are not Hadoop gurus, choosing MapR as your Hadoop vendor will have a steeper learning curve as you will need to do more training and build more admin consoles for them.
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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

  • Providing user friendly tools for hdfs access
  • More simpler apis for easy access and processsing
  • Memory requirenent
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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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We supported all three Hadoop vendors with our Hadoop RDBMS product. Here's how I see the commercial Hadoop distribution world. If you need raw performance and don't mind proprietary technology, go with MapR. If you care about the most pure open source, go with Hortonworks. If you like web UI's and ease of use, go with Cloudera.
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Return on Investment

  • It was obviously cheaper and convenient to use as most of our data processing and pipelines are on AWS. It was fast and readily available with a click and that saved a ton of time rather than having to figure out the down time of the cluster if its on premises.
  • It saved time on processing chunks of big data which had to be processed in short period with minimal costs. EMR solved this as the cluster setup time and processing was simple, easy, cheap and fast.
  • It had a negative impact as it was very difficult in submitting the test jobs as it lags a UI to submit spark code snippets.
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  • We were selling our Hadoop RDBMS as a traditional software product and there were several prospects who were interested because we supported MapR since MapR has a strong reputation for performance. I cannot comment on whether those deals were closed or not.
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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