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Amazon EMR vs Apache Hadoop comparison

 

Comparison Buyer's Guide

Executive Summary

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

ROI

Sentiment score
4.8
Amazon EMR offers cost savings and ROI benefits, with some users experiencing up to 20% cost reduction and high returns.
Sentiment score
5.4
Apache Hadoop offers cost-effective storage and processing, benefiting some with analytics and optimizing data applications for resource savings.
 

Customer Service

Sentiment score
7.9
Amazon EMR customer service varies, with generally responsive support despite reported delays and occasional gaps in integration assistance.
Sentiment score
6.1
Customer service for Apache Hadoop varies, with differing satisfaction levels and reliance on external resources and forums for support.
They help with billing, cost determination, IAM properties, security compliance, and deployment and migration activities.
Lead AWS Data Engineer at Fission Labs
We get all call support, screen sharing support, and immediate support, so there are no problems.
Senior Chief Engineer (Enterprise System Presales/Postsales) at a tech vendor with 10,001+ employees
I would rate the technical support from Amazon as ten out of ten.
Senior Technical Engineer at a transportation company with 5,001-10,000 employees
It's not structured support, which is why we don't use purely open-source projects without additional structured support.
Financial Advisor at a financial services firm with 10,001+ employees
 

Scalability Issues

Sentiment score
7.4
Amazon EMR efficiently scales for businesses, offering customizable cluster options to manage diverse data sizes and enterprise demands.
Sentiment score
7.4
Apache Hadoop is valued for its scalability, supporting large data and users effectively, especially in cloud environments.
Scalability can be provisioned using the auto-scaling feature, EC2 instances, on-demand instances, and storage locations like block storage, S3, or file storage.
Lead AWS Data Engineer at Fission Labs
It is a distributed file system and scales reasonably well as long as it is given sufficient resources.
Financial Advisor at a financial services firm with 10,001+ employees
 

Stability Issues

Sentiment score
7.7
Amazon EMR is praised for stability and reliability, with high ratings due to its configurability and robust features.
Sentiment score
7.1
Apache Hadoop is stable and reliable in multi-node clusters, performing well with minimal instability during high-load operations.
Regular updates, patch installations, monitoring, logging, alerting, and disaster recovery activities are crucial for maintaining stability.
Lead AWS Data Engineer at Fission Labs
Continuous management in the way of upgrades and technical management is necessary to ensure that it remains effective.
Financial Advisor at a financial services firm with 10,001+ employees
 

Room For Improvement

Amazon EMR users face challenges with customization, stability, onboarding, cost optimization, task speed, and demand enhanced integration and security.
Apache Hadoop needs user-friendly enhancements, better integration, improved security, streamlined setup, and modernized features and support.
The cost factor differs significantly. When you run Spark application on EKS, you run at the pod level, so you can control the compute cost. But in Amazon EMR, when you have to run one application, you have to launch the entire EC2.
Senior Chief Engineer (Enterprise System Presales/Postsales) at a tech vendor with 10,001+ employees
There is room for improvement with respect to retries, handling the volume of data on S3 buckets, cluster provisioning, scaling, termination, security, and integration between services like S3, Glue, Lake Formation, and DynamoDB.
Lead AWS Data Engineer at Fission Labs
I have thoughts on what would be great to see in the product, such as AI/ML features or additional options.
Senior Technical Engineer at a transportation company with 5,001-10,000 employees
The problem with Apache Hadoop arose when the guys that originally set it up left the firm, and the group that later owned it didn't have enough technical resources to properly maintain it.
Financial Advisor at a financial services firm with 10,001+ employees
 

Setup Cost

Amazon EMR pricing is variable, potentially costly, but users can manage expenses with strategic resource and instance management.
Enterprise Apache Hadoop pricing varies greatly, influenced by distribution choice, deployment type, and specific usage requirements.
Cost optimization can be achieved through instance usage, cluster sharing, and auto-scaling.
Lead AWS Data Engineer at Fission Labs
I would rate the price for Amazon EMR, where one is high and ten is low, as a good one.
Senior Technical Engineer at a transportation company with 5,001-10,000 employees
 

Valuable Features

Amazon EMR offers scalable, cost-effective big data management with integration, flexibility, security, and seamless Hadoop and Spark processing.
Apache Hadoop offers scalable, cost-effective data processing, supporting diverse environments with fault tolerance, integration, and analytics tools like Hive.
Amazon EMR helps in scalability, real-time and batch processing of data, handling efficient data sources, and managing data lakes, data stores, and data marts on file systems and in S3 buckets.
Lead AWS Data Engineer at Fission Labs
Amazon EMR provides out-of-the-box solutions with Spark and Hive.
Senior Chief Engineer (Enterprise System Presales/Postsales) at a tech vendor with 10,001+ employees
The features at Amazon EMR that I have found most valuable are fully customizable functions.
Senior Technical Engineer at a transportation company with 5,001-10,000 employees
Hadoop is a distributed file system, and it scales reasonably well provided you give it sufficient resources.
Financial Advisor at a financial services firm with 10,001+ employees
I assess Apache Hadoop's fault tolerance during hardware failures positively since we have hardware failover, which works without problems.
Principle Network and Database Engr at Parsons Corporation
 

Categories and Ranking

Amazon EMR
Average Rating
7.8
Reviews Sentiment
7.0
Number of Reviews
25
Ranking in other categories
Hadoop (3rd), Cloud Data Warehouse (13th)
Apache Hadoop
Average Rating
8.0
Reviews Sentiment
6.6
Number of Reviews
41
Ranking in other categories
Data Warehouse (7th)
 

Featured Reviews

reviewer1343079 - PeerSpot reviewer
Senior Chief Engineer (Enterprise System Presales/Postsales) at a tech vendor with 10,001+ employees
Has simplified ETL workflows with on-demand processing but needs improved cost efficiency and visibility
I have used AWS Glue with S3 for making tables and databases, but regarding Amazon EMR, I do not remember much as we are currently using it very minimally. This is my observation: In EKS, we have had to deploy by ourselves because EKS does not provide the Hadoop framework, Spark, Hive, and everything, but we have completed all the deployment ourselves. Whereas Amazon EMR provides all these things. The cost factor differs significantly. When you run Spark application on EKS, you run at the pod level, so you can control the compute cost. But in Amazon EMR, when you have to run one application, you have to launch the entire EC2. In Qubole, the interface was very good. I could see many details because in Amazon EMR console, very few details are available. In Qubole, at one link, you can get all the details of what is happening, how the processes are running, and the cost decreased by using Qubole. I found Qubole more user-friendly and cost-effective. From the security point of view, we had to open some access rights to Qubole, which might be a drawback in comparison to Amazon EMR which is native to AWS.
NR
Financial Advisor at a financial services firm with 10,001+ employees
Reliable performance maintained but requires ongoing management and support
Hadoop was used for years, but there were problems since the people who originally set it up left the firm. The group that owned it later didn't have the technical resources to properly maintain it. Although there was nothing wrong with Hadoop itself, issues arose without proper management and upgrades.
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Top Industries

By visitors reading reviews
Financial Services Firm
22%
Educational Organization
13%
Computer Software Company
7%
Healthcare Company
7%
Financial Services Firm
34%
Computer Software Company
7%
University
5%
Manufacturing Company
5%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business6
Midsize Enterprise5
Large Enterprise12
By reviewers
Company SizeCount
Small Business14
Midsize Enterprise8
Large Enterprise21
 

Questions from the Community

What is your experience regarding pricing and costs for Amazon EMR?
I would rate the price for Amazon EMR, where one is high and ten is low, as a good one.
What needs improvement with Amazon EMR?
I feel some lack of functionality in Amazon EMR. I have thoughts on what would be great to see in the product, such as AI/ML features or additional options.
What advice do you have for others considering Amazon EMR?
I find it easy to integrate Amazon EMR with other AWS services like S3 or EC2 for data processing needs. I would rate this review as eight out of ten.
What do you like most about Apache Hadoop?
It's primarily open source. You can handle huge data volumes and create your own views, workflows, and tables. I can also use it for real-time data streaming.
What is your experience regarding pricing and costs for Apache Hadoop?
The product is open-source, but some associated licensing fees depend on the subscription level. While it might be free for students, organizations typically need to pay for their subscriptions. Th...
What needs improvement with Apache Hadoop?
The problem with Apache Hadoop arose when the guys that originally set it up left the firm, and the group that later owned it didn't have enough technical resources to properly maintain it. This wa...
 

Comparisons

 

Also Known As

Amazon Elastic MapReduce
No data available
 

Overview

 

Sample Customers

Yelp
Amazon, Adobe, eBay, Facebook, Google, Hulu, IBM, LinkedIn, Microsoft, Spotify, AOL, Twitter, University of Maryland, Yahoo!, Cornell University Web Lab
Find out what your peers are saying about Amazon EMR vs. Apache Hadoop and other solutions. Updated: December 2025.
881,082 professionals have used our research since 2012.