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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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
The AWS stack is a big component of the majority of our work. When necessary, EMR is employed in a number of these settings. When we need to process a large amount of data across several EC2 servers, our DevOps team implements it. For our customers, EMR is attractive since it is far less expensive to adopt than alternative solutions, which means that the overall cost savings are substantial.
Score 7 out of 10
Vetted Review
Verified User
Review Source
We use Amazon EMR (Elastic MapReduce) to run various types of algorithms related to health like calculation of body mass index, heart rate and similar parameters on vast amounts of data. We do this for developing a prototype of a health analysis device that users can wear on their body - something like a smart watch fitness tracker.
Score 8 out of 10
Vetted Review
Verified User
Review Source
We started using Amazon EMR because the query time was too high in EC2 instances. When we moved to process our Hive-based clusters data to Amazon EMR, the time taken to execute big data substantially dropped. It's a great tool to reduce the query processing time for Spark and Hive so naturally, we migrated our entire framework on EC2 to EMR. We are currently using EMR to do Hadoop file processing to process data from S3 and EC2.
Score 8 out of 10
Vetted Review
Verified User
Review Source
For some clients, we have our product hosted on several AWS products, and when it comes to retrieving big volumes of data we use the Amazon EMR service. It has aided us in becoming more productive and saving time and effort. AWS is our go-to service for most of our needs,
José David Rodríguez Gómez | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
On request transitory clusters for huge information handling. I like its accessibility completely different taken a toll tire makes it greatly flexible for distinctive scale clients. Can be pre-installed with any Huge information apparatuses like Hive, Start, Pig, etc. Nitty-gritty cluster observing makes a difference to track a few measurements, in turn, makes a difference to diminish fetched.
Score 9 out of 10
Vetted Review
Verified User
Review Source
Having our product hosted on various AWS EC2 instances for some clients and when it requires pulling large amounts of data and performing large transformations using client data, we would use the amazon EMR service to get that work done. The usage is limited to a few clients of our product rather than the entire client base.
Score 10 out of 10
Vetted Review
Verified User
Review Source
We migrated the entire hadoop structure to Amazon EMR, the cost and maintenance are much better compared to other solutions on the market. We created a recommender system filter in big data. We needed a low runtime to meet our demand and we were able to get through the Amazon EMR.We migrated the entire hadoop structure to Amazon EMR, the cost and maintenance are much better compared to other solutions on the market. We have a lot of data science tasks, like calculating statistics between various math calculations to apply the business rules. Definitely one of the best services to work on bigdata.
Nick Waters | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
I use Amazon EMR (Elastic Map Reduce) as a scalable platform to deploy my client solutions onto. It allows me to scale our solution elastically in the cloud and allows us to deal with any data size, volume, or complexity. It is very easy to configure and scale and it is my preferred platform to deploy to.
April 06, 2022

AWS has it all!

Jonathan Brotto | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Review Source
To keep my review simple it is very convenient that AWS has a MapReduce tool as it was easy to deploy and test with our cloud setup. Also with AWS being well known it is easy to find staff who can use and set up a system and scale our solutions. Definitely an industry leader.
Score 7 out of 10
Vetted Review
Verified User
Review Source
We use it to process our data for real-time prediction for one of our AI models. This has tremendously reduced our effort and time. We have integrated this service with our product lines as well to process large amounts of contacts and generate an AI-based score to prioritize the contacts.
Score 8 out of 10
Vetted Review
Verified User
Review Source
Most commonly we use Amazon ec2 for daily work on the cloud but when it comes to pulling large amounts of data and performing large transformations using spark, we would use the amazon EMR service to get that work done. The usage is limited to a few departments rather than the entire company.
Uddipan Mukherjee | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
Used as spark cluster to enable Big data ETL processes. Analysists and data scientists uses clusters for adhoc querying purposes. Raw data ingestion fro. RDBMS systems , APIs, file systems etc. Used elastic feature with different node types to optimize cost. Scope of the use case is a company wide big data platform.
September 22, 2020

Amazon EMR Review

Score 7 out of 10
Vetted Review
Verified User
Review Source
Amazon EMR is being used by our organization to simplify running big data frameworks, and provide the Amazon EMR highlights, product details, and pricing information. It is used across the whole organization and is enjoyed by everyone. It addresses business problems like slow running big data frameworks and not being provided highlights, product details, and pricing information for Amazon EMR.
Nicolas Costa Ossa | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
We are a certified AWS partner agency, and we use a lot of the AWS stack for most of our projects. EMR is used in several of them when required. It is implemented by our DevOps team and we pretty much use it when we need to process a lot of data throughout EC2 instances. EMR is very compelling to our customers because it is easier to implement (hence less dev cost) and it is way more efficient when managing the data VS other tools, so the overall cost reduction is considerable.
Score 8 out of 10
Vetted Review
Verified User
Review Source
Amazon Eliastic MapReduce may be a mouthful (EMR is much easier to say) but like taking that string and reducing it to its acronym, it takes a complex set of data and reduces to something manageable and understandable. Its been deployed as a solution to massive, and spread out data that needs to be consolidated.
Thomas Young | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
Amazon Elastic MapReduce is used by my department to produce big data analytics for certain clients. The software address data mining and predictive analytics for data sets that take a long time to process. The software is not used for econometric or other analytical evaluation because the size of the data sets does not lend themselves to such analysis. The software is used almost exclusively for data mining and simple reporting for large data cases.
Score 9 out of 10
Vetted Review
Verified User
Review Source
We use Amazon EMR for big data storage and processing. It's cluster architecture with each department having different clusters. It's great for processing and storage of large volumes of data, specifically, the data which is unstructured and generates very rapidly, like network logs.
November 17, 2017

EMR review

Score 8 out of 10
Vetted Review
Verified User
Review Source
EMR is being used by our department, not the whole organization. We use it as the infrastructure on which we run Spark jobs. Those jobs are mainly used for data I/O, data processing, and machine learning applications.
October 25, 2017

AWS EMR at a glance!!

Score 7 out of 10
Vetted Review
Verified User
Review Source
We have used AWS EMR before starting to use Databricks on EC2 instances. EMR was solving the problem but we needed a better solution (Enterprise edition) to manage our Workbooks and better scheduler for running or jobs. EMR was working fine but we did not find it user friendly to add the data nodes on demand. We used EMR primarily to process the data on AWS S3 using Hadoop and Spark frameworks. We have also used AWS SWF to orchestrate our job flow by adding steps. It was used widely by the data processing team and not by the entire organization as most of the data was on local servers. It addresses problems like processing data which might not need to be processed live as the cluster can be spun up and shut down once the job is completed. It is cost efficient (especially if you do not need data nodes and only task nodes), scalable and reliable.
Score 6 out of 10
Vetted Review
Verified User
Review Source
As a PhD student, I used Amazon Elastic MapReduce for my research for analyzing my data. Firstly, it was very scalable and did not cause much performance impact when using large data sets. Secondly, their web console is very easy to use and intuitive. There were many resources that could be used whenever I encountered any problems with EMR.