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Score 8.3 out of 101
103 Ratings
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Score 8.5 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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Apache Spark

The software appears to run more efficiently than other big data tools, such as Hadoop. Given that, Apache Spark is well-suited for querying and trying to make sense of very, very large data sets. The software offers many advanced machine learning and econometrics tools, although these tools are used only partially because very large data sets require too much time when the data sets get too large. The software is not well-suited for projects that are not big data in size. The graphics and analytical output are subpar compared to other tools.
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Pros

  • Distributed computing
  • Fault tolerant
  • Uptime
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  • Rich APIs for data transformation making for very each to transform and prepare data in a distributed environment without worrying about memory issues
  • Faster in execution times compare to Hadoop and PIG Latin
  • Easy SQL interface to the same data set for people who are comfortable to explore data in a declarative manner
  • Interoperability between SQL and Scala / Python style of munging data
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Cons

  • The analytical processes generally run quicker with the standalone tools of Hadoop, Spark, and others. If you only use one big data tool and don't really need things simplified, then Elastic MapReduce is more of an overhead tool that doesn't add much value.
  • The analytical capabilities of Elastic MapReduce are nowhere near as complex or broad as non-big data tools. I would suggest not using the tool unless your data really is big data.
  • The machine learning capabilities of Elastic MapReduce (using the big data tools of Hadoop/Spark) are good but are not as easy to use as other machine learning tools.
Thomas Young profile photo
  • could do a better job for analytics dashboards to provide insights on a data stream and hence not have to rely on data visualization tools along with spark
  • also there is room for improvement in the area of data discovery
Shiv Shivakumar profile photo

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 evaluated SAS alongside with Apache Spark but during the course of proof of concept found that Apache Spark was able to support the hadoop eco-system and hadoop file system much better. It was much faster at that time while having the ability to process data quickly for the business analytical needs and and also scaled up well.
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Return on Investment

  • Amazon Elastic MapReduce has had a positive ROI in the sense that it saved time managing big data projects where analysts were using different big data tools. Essentially, an increase in employee productivity.
  • Elastic MapReduce is not worth it in cases where you're just trying things out. You'll likely lose money unless you're sure that using MapReduce is a good idea.
  • Elastic MapReduce takes some time learning, although not too much. If the employee is less well-versed in big data analytics, the software is a high hill to climb that eats up employee time.
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  • Positive: we don't worry about scale.
  • Positive: large support community.
  • Negative: Takes time to set up, overkill for many simpler workflows.
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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

Apache Spark

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