Amazon EMR

Amazon EMR

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Score 8.5 out of 100
Amazon EMR (Elastic MapReduce)

Overview

Recent Reviews

Amazon EMR - fast, and elastic

7 out of 10
April 19, 2022
We use Amazon EMR (Elastic MapReduce) to run various types of algorithms related to health like calculation of body mass index, heart rate …
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Amazon EMR Review

7 out of 10
September 22, 2020
Amazon EMR is being used by our organization to simplify running big data frameworks, and provide the Amazon EMR highlights, product …
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What is Amazon EMR?

Amazon EMR is a cloud-native big data platform for processing vast amounts of data quickly, at scale. Using open source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi (Incubating), and Presto, coupled with the scalability of Amazon EC2 and scalable storage of Amazon…

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  • No setup fee

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  • Free/Freemium Version
  • Premium Consulting / Integration Services

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Product Details

What is Amazon EMR?

Amazon EMR is a cloud-native big data platform for processing vast amounts of data quickly, at scale. Using open source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi (Incubating), and Presto, coupled with the scalability of Amazon EC2 and scalable storage of Amazon S3, EMR gives analytical teams the engines and elasticity to run Petabyte-scale analysis.

Amazon EMR Technical Details

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Frequently Asked Questions

What is Amazon EMR?

Amazon EMR is a cloud-native big data platform for processing vast amounts of data quickly, at scale. Using open source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi (Incubating), and Presto, coupled with the scalability of Amazon EC2 and scalable storage of Amazon S3, EMR gives analytical teams the engines and elasticity to run Petabyte-scale analysis.

What is Amazon EMR's best feature?

Reviewers rate Support Rating highest, with a score of 9.

Who uses Amazon EMR?

The most common users of Amazon EMR are from Mid-sized Companies (51-1,000 employees) and the Computer Software industry.

Reviews and Ratings

 (57)

Ratings

Reviews

(1-20 of 20)
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Score 8 out of 10
Vetted Review
Verified User
Review Source
  • Faster than prior on-premise systems to put in place.
  • Open source software is supported.
  • Reduces the cost of production.
  • Automation of processing jobs creation and deletion.
  • The cost of this service is more expensive than similar ones.
  • Getting everything up and running at the beginning is a lengthy process.
José David Rodríguez Gómez | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
  • Big data processing.
  • The resizing feature is good.
  • Ease of use and creating new clusters.
  • The user interface could use a facelift.
  • Overhead delay in starting clusters.
  • Big learning curve for someone who hasn't used a program like this before.
September 22, 2020

Amazon EMR Review

Score 7 out of 10
Vetted Review
Verified User
Review Source
  • Provides the Amazon EMR highlights, product details, and pricing information.
  • Simplifies running big data frameworks.
  • Analyzes vast amounts of data.
  • Freezes sometimes.
  • Glitches a lot.
  • Runs slow.
Nicolas Costa Ossa | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
  • Easier to implement than older on-premise solutions
  • Works with open source technologies.
  • Keeps processing cost low.
  • It is flexible and works also for short term workloads and the pricing changes to that model.
  • You definitely need to be trained before using it because the interface can be a little confusing. It is a professional service model, so I recommend a certified dev.
Thomas Young | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
  • Amazon Elastic MapReduce works well for managing analyses that use multiple tools, such as Hadoop and Spark. If it were not for the fact that we use multiple tools, there would be less need for MapReduce.
  • MapReduce is always on. I've never had a problem getting data analyses to run on the system. It's simple to set up data mining projects.
  • Amazon Elastic MapReduce has no problems dealing with very large data sets. It processes them just fine. With that said, the outputs don't come instantaneously. It takes time.
  • 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.
November 17, 2017

EMR review

Score 8 out of 10
Vetted Review
Verified User
Review Source
  • Ease of use and ease to setup
  • Autoscaling functionality
  • Integrated into the AWS environment
  • Cost overhead is a bit high
  • Limited versions of frameworks that can be used
October 25, 2017

AWS EMR at a glance!!

Score 7 out of 10
Vetted Review
Verified User
Review Source
  • EMR does well in managing the cost as it uses the task node cores to process the data and these instances are cheaper when the data is stored on s3. It is really cost efficient. No need to maintain any libraries to connect to AWS resources.
  • EMR is highly available, secure and easy to launch. No much hassle in launching the cluster (Simple and easy).
  • EMR manages the big data frameworks which the developer need not worry (no need to maintain the memory and framework settings) about the framework settings. It's all setup on launch time. The bootstrapping feature is great.
  • 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.
Score 6 out of 10
Vetted Review
Verified User
Review Source
  • The cluster size of MapReduce is very dynamic and therefore scalability is good for EMR.
  • It also works well with other Amazon Web Services like Amazon Simple Storage Service, which means that data can be taken from those services and written back to them.
  • I tried using the in-house hosting at the university I work in, but there would be a lot of complications with technical support required. For Amazon, the support and documentation was good to solve these problems faster.
  • It would have been better if packages like HBase and Flume were available with Amazon EMR. This would make the product even more helpful in some cases.
  • Products like Cloudera provide the options to move the whole deployment into a dedicated server and use it at our discretion. This would have been a good option if available with EMR.
  • If EMR gave the option to be used with any choice of cloud provider, it would have helped instead of having to move the data from another cloud service to S3.