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Amazon Elastic MapReduce (EMR) is a web service for processing big data (hadoop).https://dudodiprj2sv7.cloudfront.net/product-logos/96/0p/49F4QV1KRH9P.pngAmazon EMR- Great cloud based Hadoop platformWe 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,9,Better accesss to business data Faster business decisions Better storage and processingEMR reviewEMR 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,8,It was easy to set up initial versions of Spark on this Still used as our compute platform as its easy to manage Certain times we forgot to shut down clusters and were overcharged,Databricks, Cloudera Enterprise and Hortonworks Data Platform,Amazon S3 (Simple Storage Service), Amazon Relational Database Service, Apache Spark, Cassandra, Apache KafkaAWS EMR at a glance!!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.,7,It was obviously cheaper and convenient to use as most of our data processing and pipelines are on AWS. It was fast and readily available with a click and that saved a ton of time rather than having to figure out the down time of the cluster if its on premises. It saved time on processing chunks of big data which had to be processed in short period with minimal costs. EMR solved this as the cluster setup time and processing was simple, easy, cheap and fast. It had a negative impact as it was very difficult in submitting the test jobs as it lags a UI to submit spark code snippets.,Databricks and Hortonworks Data Platform,Databricks, Amazon Elastic Compute Cloud (EC2), Amazon DynamoDB, Amazon S3 (Simple Storage Service), Amazon Aurora, Amazon Redshift, Amazon CloudFront, Amazon CloudWatchResearch Experience with Amazon EMRAs 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.,6,Positive: Helped process the jobs amazingly fast. Positive: Did not have to spend much time to learn the system, therefore, saving valuable research time. Negative: Not flexible for some scenarios, like when some plugins are required, or when the project has to be moved in-house.,Cloudera,Cloudera Enterprise
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Amazon Elastic MapReduce
24 Ratings
Score 8.3 out of 101
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Amazon EMR Reviews

Amazon EMR
24 Ratings
Score 8.3 out of 101
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Reviews (1-4 of 4)
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January 18, 2018

Review: "Amazon EMR- Great cloud based Hadoop platform"

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.
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November 17, 2017

Amazon EMR: "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.
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October 25, 2017

Amazon EMR Review: "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.
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June 22, 2016

User Review: "Research Experience with Amazon EMR"

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.
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Amazon EMR Scorecard Summary

About Amazon EMR

Amazon Elastic MapReduce (EMR) is a web service for processing big data (hadoop).
Categories:  Hadoop-Related

Amazon EMR Technical Details

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