Overview
Product Information on Amazon EMR
Updated 13th October 2025
What is Amazon EMR?
Amazon EMR Pricing
Overall experience with Amazon EMR
Director of Engineering
250M - 500M USD, BankingFAVORABLE
“EMR Delivers Strong Compute Capacity for Pyspark-Based Data Processing Tasks”
EMR provides high compute capacity for our data processing needs. We have several models being executed on EMR using Pyspark
Data Analyst
250M - 500M USD, RetailCRITICAL
“Awesome product but needs some changes”
good, but can be better with more features and controls for engineers
About Company
Company Description
Amazon Web Services (AWS), established in 2006, is focused on providing essential infrastructure services to businesses globally in the form of cloud computing. The key advantage offered through cloud computing, particularly via AWS, is its capacity to shift fixed infrastructure expenses into flexible costs. Businesses have been able to forgo extensive planning and procurement of servers and other Information Technology (IT) resources, owing to AWS. AWS seeks to provide businesses with prompt and cost-effective access to resources using Amazon's expertise and economies of scale, as and when their business requires. Currently, AWS offers a robust, scalable, economic infrastructure platform on the cloud powering an extensive array of businesses worldwide. It operates across numerous industries with data center locations in various parts of the globe including U.S., Europe, Singapore, and Japan.
Company Details
Company type
Public
Year Founded
2006
Head office location
Seattle, United States
Number of employees
10001+
Website
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Reviewer Insights for: Amazon EMR
Performance of Amazon EMR Across Market Features
Amazon EMR Likes & Dislikes
Great high compute platform
hands on, configurability
using Hadoop based on pay per use.
not much i can think off.
finding failure is hard
technology is quite old now for map reduce.
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- Director of Engineering50M-1B USDBankingReview Source
EMR Delivers Strong Compute Capacity for Pyspark-Based Data Processing Tasks
EMR provides high compute capacity for our data processing needs. We have several models being executed on EMR using Pyspark - VP, Data and AnalyticsGov't/PS/EdGovernmentReview Source
Platform Utilizes Map Reduce Components and Supports Spot CPU Usage
the platform is consist of standard of map reduce components and can use spot CPU - Engineer<50M USDBankingReview Source
Comprehensive Spark Configurations Available Through Flexible API Calls in This Product
very complete, i can work better spark configs with api calls than in other services - Data Analyst50M-1B USDRetailReview Source
Awesome product but needs some changes
good, but can be better with more features and controls for engineers - Senior Director Of Technology1B-10B USDIT ServicesReview Source
Amazon EMR’s Impact on Cost Savings and Reporting Service Performance Improvements
My organization has utilized Amazon EMR for about 45 days, and the overall experience has been great. We chose EMR due to our existing Amazon Heavy infrastructure, evaluating AWS solutions over external vendors. EMR provided more flexibility in cost management than auto-scaling groups, especially with its dynamic handling of node counts and mixing on-demand and spot instances. EMR addressed key business pain points: Firstly, it delivered significant cost savings. We reduced daily costs by 40-45% for a service previously spending on 100% on-demand instances, by implementing an 80/20 distribution of spot to on-demand nodes. This projects potential savings per month if all our Hadoop loads transition to EMR. Other teams have also started using EMR for heavy Hadoop loads due to its cost optimization. Secondly, EMR vastly improved our heavy reporting service for customers, which previously suffered from report queuing and throttling during peak morning hours. Our report response time improved dramatically, from approximately 30 mins to 2 mins. The system spins up additional nodes when reports are triggered and shuts them down during lean periods, preventing cost incurrence. This directly led to the retention of a significant FMCG giant customer in the US, a contract that could have resulted in over a 5% loss of our overall revenue, with minimal deployment effort. From an onboarding perspective, my prior experience with EMR made the process straightforward. AWS offers extensive use cases, design diagrams, and paid support typically responds within 2-24 hours. Customization was not a major concern, as EMR’s features and documentation meet most industry needs. EMR efficiently handles large-scale data processing, with configurable node limits and built-in fault tolerance. A caution regarding performance: for very large node during peak hours, it's advised to maintain at least a 50/50 split between spot and on-demand instances to avoid availability issues.
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