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.
  • 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.
You can use Amazon EMR if you wish to shift to the cloud and save money by using Apache Spark or Apache Hadoop on-premises. When the amount of work you have to handle data fluctuates a lot. Setting up flexible and scalable scenarios with AWS's EMR can assist you.
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.
  • They have excellent tech support
  • Reduced processing times
  • Easy to configure
  • Pricing should be better
  • User Interface should be more attractive
  • Faster ramp up
Scenarios where it is good:
1. Where speed is important, and there is a vast amount of data to process
2. Configuration setup needs to be fast

Scenarios where it is not good:
1. For small companies which do not have enough money
2. For one-off uses, since the ramp up curve is high
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.
  • Process Big Data
  • EC2 and S3 Big Data Processing (Hadoop)
  • Reduce processing time substantially
  • Automation in Creation and Deletion of Processing Jobs
  • Costly - Priced on higher end in comparison to other services
  • Initially setup takes time
Amazon EMR is well suited in scenarios where
> You require Hadoop-based processing, and time is a constraint.
> Need a platform that supports Spark and Hive at the same time.
> Flexible resizing options are available - So it's a tool for conditions requiring an increase/decrease in cores. > Fast big data processing is a requirement.

I would look for other options if
> The job budget is limited, and time could be flexible
> Initialization (Starting the job) and Terminating the job require automatic interference (As with EMR, you will need to intervene manually).
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,
  • very easy to configure
  • easy to manage large amounts of data
  • very quick in executing transformations
  • not a recommended service to mange smaller amounts of data
  • expensive
  • not the best user interface out there
Our teams prefer using this service to deploy because it is simple to configure and scale even though it can be expensive at times. It also needs some training for new users to get familiar with all the functions and features. Experience matters a lot while using this platform.
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.
  • 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.
We are running it to perform preparation which takes a few hours on EC2 to be running on a spark-based EMR cluster to total the preparation inside minutes rather than a few hours. Ease of utilization and capacity to select from either Hadoop or spark. Processing time diminishes from 5-8 hours to 25-30 minutes compared with the Ec2 occurrence and more in a few cases.
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.
  • Manage large database well.
  • Usage Monitoring.
  • Quick
  • Cost effective.
  • Little bit complex with setting it up.
  • Could be costly of not configured well.
  • Typical to manage.
If someone is playing with a large dataset over AWS it's worth using it for a small kind of dataset it doesn't make sense as it's complex to manage.
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.
  • Faster processing.
  • The distributed computation of the calculations.
  • Easy to setup.
  • Monitoring as an add up.
  • Can be integrated with lots of technologies.
  • Overhead delay in starting clusters which can cause problems.
It provides a nice graphical user interface to manage and work with big data map reduction tasks instead of manual configuration with hadoop or cli.it saves a lot of time and effort.We create big data monitoring system filters.

It provides a good GUI to manage and handle big data map reduction tasks and its configuration saves a lot of time and effort.
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.
  • Scalability
  • Costings
  • Flexibility
  • Costs
  • Auto-scale
Amazon EMR (Elastic Map Reduce) is ideally suited for organisations that need to provide a scalable platform for data processing. Amazon EMR is perfect to handle data volumes of any size or volume and it is easy to configure and spin up an EMR cluster quickly. If you are cost-focused you need to be careful as an EMR cluster can end up costing a lot of money if misconfigured.
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.
  • Scalable
  • Flexible
  • Good documentation
  • Cost effective
  • Integration with ERP for SMEs.
  • To connect to non cloud solutions and replicate data for backup.
  • Better performance metrics for business people such as cost benefits.
When I need to process large data and meaningful information. But it is very flexible where I can scale based on the data size and how I want to analyze it. But still can improve for nontechnical users as there is some jargon to learn to get the most out of the solution.
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.
  • Process large data seamlessly.
  • Easy to integrate with other services.
  • Very time and cost effective.
  • Hard to manage.
  • It can suggest cost saving tricks because it can be costly if not done right.
  • should be able replicate previous steps.
It is a go-to software for experienced developers and for a company that wishes to process huge amounts of data at scale.
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.
  • Quick.
  • Massive.
  • Relatively cheap.
  • Harder to manager than ec2.
  • Could be very costly if not doing right.
  • RAM is fixed.
It has its use case. When it comes to extremely large datasets' pulling and transformation, it is the best way to do it. Just need to be aware that if something is not set correctly in the first place, the cost could potentially be very large. Therefore, it is very critical to not do development work there.
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.
  • Big data ETL
  • Data ingestion
  • Ad hoc query support
  • Library management
  • Storing historical steps
  • Downloading EMR job logs could be easier
Amazon EMR is well suited for Big data ETL, workflow management, eco system integration.

May not be suitable for running ML workloads.
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.
  • 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.
Some scenarios where Amazon EMR is well suited include simplifying running big data frameworks, providing the Amazon EMR highlights, product details, and pricing information, and analyzing vast amounts of data. Some scenarios where Amazon EMR is less appropriate include assisting clients with problems on their servers, and coding our clients' many servers at our data centers.
I give the overall support for Amazon EMR this rating because while the support technicians are very knowledgeable and always able to help, it sometimes takes a very long time to get in contact with one of the support technicians. So overall the support is pretty good 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.
  • 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.
For example, when you have Apache Spark on-premise deployments, or also Apache Hadoop, and you want to move to the cloud and reduce costs, EMR is the right tool. When you have lots of ups and downs in workload levels to process data. AWS's EMR can help you by setting up flexible/scalable scenarios.
There's a vast group of trained and certified (by AWS) professionals ready to work for anyone that needs to implement, configure or fix EMR. There's also a great amount of documentation that is accessible to anyone who's trying to learn this. And there's also always the help of AWS itself. They have people ready to help you analyze your needs and then make a recommendation.
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.
  • Makes massive data easier to manage
  • Backed by Amazon and AWS
  • Makings analyzing data easy
  • Support more data frameworks
  • API integration
  • Cloud service integration
Amazon is the big player in the data game right now as it even seems to push Google out of the way in some instances. Because of this you know they treat your data well and also deal with a ton themselves. That makes them good at a comparably smaller data set like most companies have.
AWS and EMR support are on par with the best out there. You pay a premium for the support but they can save you time and money by quickly resolving issues or helping you get your problem taken care of. They are competing with Google and MS, and it shows in their support.
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.
  • 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.
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.
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.
  • Distributed computing
  • Fault tolerant
  • Uptime
  • Providing user friendly tools for hdfs access
  • More simpler apis for easy access and processsing
  • Memory requirenent
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.
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.
  • 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
Well suited if you quickly want to setup a distributed compute platform, such as Spark. But you have to be advanced enough that you really want to separate compute from data storage. For example, for certain applications packaged solution such as MPP databases (e.g. Redshift) is much easier to set up that Spark on EMR and S3 with the appropriate file formats.
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.
  • 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.
EMR is suited if the jobs are long running and doesn't really need much monitoring. EMR is really flexible in processing the data on s3 as a developer doesn't need to spend time on debugging the connections to s3 from a big data framework as most of the configuration is taken care of by Amazon. Very cheap when compared to most of the solutions on the market and the ready to go configuration at the launch time reduces the amount of time required for admin tasks. So, considering the cheap cost, processing options on s3 and scalability via adding task nodes, EMR serves a better purpose for startups considering open source and cost efficient options.

However, EMR comes with its own disadvantages. There is no proper UI to track real time jobs which is however possible with Enterprise editions like Cloudera, Hortonworks etc. EMR could provide an interface to add workbooks and code snippets in the cluster as it would reduce the time to submit the tasks. EMR also lags the potential to automatically replace unhealthy nodes.
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.
  • 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.
If the person using EMR does not need much customization, like debugging or other modifications, or the data is not entirely in another cloud, then Amazon Elastic MapReduce is a better option. Otherwise, there are other open source projects available like Cloudera that are available to be used. Products like Cloudera can also be deployed in any cloud, rather than having to stick with Amazon.