What users are saying about
27 Ratings
109 Ratings
27 Ratings
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Score 8.5 out of 101
109 Ratings
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Score 8.4 out of 101

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

Amazon EMR

  • Distributed computing
  • Fault tolerant
  • Uptime
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Apache Spark

  • Apache Spark makes processing very large data sets possible. It handles these data sets in a fairly quick manner.
  • Apache Spark does a fairly good job implementing machine learning models for larger data sets.
  • Apache Spark seems to be a rapidly advancing software, with the new features making the software ever more straight-forward to use.
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Cons

Amazon EMR

  • Sometimes bootstrapping certain tools comes with debugging costs. The tools provided by some of the enterprise editions are great compared to EMR.
  • Like some of the enterprise editions EMR does not provide on premises options.
  • No UI client for saving the workbooks or code snippets. Everything has to go through submitting process. Not really convenient for tracking the job as well.
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Apache Spark

  • Apache Spark requires some advanced ability to understand and structure the modeling of big data. The software is not user-friendly.
  • The graphics produced by Apache Spark are by no means world-class. They sometimes appear high-schoolish.
  • Apache Spark takes an enormous amount of time to crunch through multiple nodes across very large data sets. Apache Spark could improve this by offering the software in a more interactive programming environment.
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Alternatives Considered

Amazon EMR

Having one of these enterprise edition license comes at its own costs. But, the flexibility to have the cluster spin up with the workbenches and code snippets on the same is really beneficial. Especially, if one had to move out of EMR and consider an option which reduces the debugging time in establishing connections to AWS resources, I would love to used the mentioned three resources on EC2. This would definitely make the processing time to reduce as there is a flexibility to test real time and execute the code snippet and look at the performance and monitor the snippet in real time.
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Apache Spark

How does Apache Spark perform against competing tools? I think Apache Spark does well in processing large volumes of data. The machine learning models also seem to be easier to program and interpret. With that said, the programming side of Apache Spark seems more difficult to implement good models than Kinesis or other tools. You really have to have lots of data and very valuable questions to answer to justify the investment in Apache Spark.
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Return on Investment

Amazon EMR

  • Better accesss to business data
  • Faster business decisions
  • Better storage and processing
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Apache Spark

  • Workflow process using spark went from 1 day to 2 hours
  • Spark Streaming allowed for quick determiniation of data validity
  • spark on yarn was good for manangement. But Spark with Kubernetes was easier to use.
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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

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