Name: Amazon EMR
Rating: 4.4 (60 reviews)

Overview

Product Information on Amazon EMR

Updated 13th October 2025

What is Amazon EMR?

Amazon EMR is a software for processing and analyzing large datasets using open-source tools such as Apache Spark, Apache Hadoop, and Presto. The software enables users to run distributed data processing workloads on scalable cloud infrastructure, automating provisioning and configuration of cluster resources. It supports a range of data analytics tasks including batch processing, machine learning workflows, and interactive SQL queries. Amazon EMR software is designed to address challenges related to managing big data environments, helping organizations reduce operational overhead and optimize resource usage for analytics and business intelligence initiatives.

Overall experience with Amazon EMR

Director of Engineering

250M - 500M USD, Banking

FAVORABLE

“EMR Delivers Strong Compute Capacity for Pyspark-Based Data Processing Tasks”

5.0

Dec 1, 2025

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, Retail

CRITICAL

“Awesome product but needs some changes”

3.0

Dec 2, 2025

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

http://aws.amazon.com

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Key Insights

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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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Peer Discussions

Amazon EMR Reviews and Ratings

Recommended Gartner Insights

Market Guide for Hadoop Distributions

50M-1B USD

Banking

Review Source

EMR Delivers Strong Compute Capacity for Pyspark-Based Data Processing Tasks

VP, Data and Analytics

Gov't/PS/Ed

Government

Platform Utilizes Map Reduce Components and Supports Spot CPU Usage

4.0

the platform is consist of standard of map reduce components and can use spot CPU

Engineer

<50M USD

Comprehensive Spark Configurations Available Through Flexible API Calls in This Product

Nov 30, 2025

very complete, i can work better spark configs with api calls than in other services

Retail

Awesome product but needs some changes

Senior Director Of Technology

1B-10B USD

IT Services

Amazon EMR’s Impact on Cost Savings and Reporting Service Performance Improvements

Jul 17, 2025

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.