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Amazon EMR vs Apache Spark vs Cloudera Distribution for 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
6.1
Apache Spark enhances machine learning, cutting operational costs by up to 50%, with efficiency reliant on resources and expertise.
Sentiment score
5.5
Measuring ROI from Cloudera Distribution for Hadoop is complex due to diverse applications, pricing, and evaluation difficulties.
 

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
5.9
Apache Spark support feedback varies, with mixed reviews on community forums, vendor support, and documentation adequacy.
Sentiment score
6.5
Cloudera's Hadoop support receives mixed reviews, with users praising responsiveness while noting concerns on quality and accessibility.
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
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
They help with billing, cost determination, IAM properties, security compliance, and deployment and migration activities.
Lead AWS Data Engineer at Fission Labs
I have received support via newsgroups or guidance on specific discussions, which is what I would expect in an open-source situation.
Data Architect at Devtech
The technical support is quite good and better than IBM.
Manager, Bussines Development & Co Owner at Troia d.o.o.
 

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
7.5
Apache Spark excels in scalability, efficiently handling large data workloads with ease, though it requires skilled infrastructure management.
Sentiment score
7.7
Cloudera Distribution for Hadoop is highly scalable and flexible, suitable for large deployments but can be costly to expand.
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
 

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
7.4
Apache Spark is generally stable, trusted by companies; newer versions enhance reliability, though memory issues may arise without proper configuration.
Sentiment score
7.3
Cloudera Distribution for Hadoop has mixed stability reviews, with hardware issues noted, but support and workarounds are available.
Regular updates, patch installations, monitoring, logging, alerting, and disaster recovery activities are crucial for maintaining stability.
Lead AWS Data Engineer at Fission Labs
Apache Spark resolves many problems in the MapReduce solution and Hadoop, such as the inability to run effective Python or machine learning algorithms.
Data Engineer at a tech company with 10,001+ employees
Without a doubt, we have had some crashes because each situation is different, and while the prototype in my environment is stable, we do not know everything at other customer sites.
Data Architect at Devtech
We faced challenges but overcame those challenges successfully.
Head of Advaced Analytics & Intelligence; AGM at Alinma Bank
 

Room For Improvement

Amazon EMR users face challenges with customization, stability, onboarding, cost optimization, task speed, and demand enhanced integration and security.
Apache Spark requires improvements in scalability, usability, documentation, memory efficiency, real-time processing, and broader language support for better performance.
Cloudera Distribution for Hadoop struggles with stability and integration, needing better performance, security, documentation, and modern deployment solutions.
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
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
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
Various tools like Informatica, TIBCO, or Talend offer specific aspects, licensing can be costly;
Data Architect at Devtech
Integrating with Active Directory, managing security, and configuration are the main concerns.
Manager, Bussines Development & Co Owner at Troia d.o.o.
 

Setup Cost

Amazon EMR pricing is variable, potentially costly, but users can manage expenses with strategic resource and instance management.
Apache Spark is cost-effective but may incur expenses from hardware, cloud resources, or commercial support, impacting deployment costs.
Cloudera's Hadoop distribution is costly, aimed at large enterprises, lacking a community version, with per-node licensing.
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
It can be deployed on-premises, unlike competitors' cloud-only solutions.
Manager, Bussines Development & Co Owner at Troia d.o.o.
 

Valuable Features

Amazon EMR offers scalable, cost-effective big data management with integration, flexibility, security, and seamless Hadoop and Spark processing.
Apache Spark offers fast in-memory processing, scalable analytics, MLlib for machine learning, SQL support, and seamless integration with languages.
Cloudera for Hadoop offers easy installation, robust security, tool integration, scalability, and supports on-premises and cloud environments.
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
We are using it to clean the data and transform the data in such a way that the end-user can get the insights faster.
Senior Technical Engineer at a transportation company with 5,001-10,000 employees
Not all solutions can make this data fast enough to be used, except for solutions such as Apache Spark Structured Streaming.
Data Engineer at a tech company with 10,001+ employees
The solution is beneficial in that it provides a base-level long-held understanding of the framework that is not variant day by day, which is very helpful in my prototyping activity as an architect trying to assess Apache Spark, Great Expectations, and Vault-based solutions versus those proposed by clients like TIBCO or Informatica.
Data Architect at Devtech
This is the only solution that is possible to install on-premise.
Manager, Bussines Development & Co Owner at Troia d.o.o.
 

Mindshare comparison

As of February 2026, in the Hadoop category, the mindshare of Amazon EMR is 10.4%, down from 14.0% compared to the previous year. The mindshare of Apache Spark is 13.4%, down from 18.4% compared to the previous year. The mindshare of Cloudera Distribution for Hadoop is 14.0%, down from 27.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop Market Share Distribution
ProductMarket Share (%)
Apache Spark13.4%
Cloudera Distribution for Hadoop14.0%
Amazon EMR10.4%
Other62.2%
Hadoop
 

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.
Devindra Weerasooriya - PeerSpot reviewer
Data Architect at Devtech
Provides a consistent framework for building data integration and access solutions with reliable performance
The in-memory computation feature is certainly helpful for my processing tasks. It is helpful because while using structures that could be held in memory rather than stored during the period of computation, I go for the in-memory option, though there are limitations related to holding it in memory that need to be addressed, but I have a preference for in-memory computation. The solution is beneficial in that it provides a base-level long-held understanding of the framework that is not variant day by day, which is very helpful in my prototyping activity as an architect trying to assess Apache Spark, Great Expectations, and Vault-based solutions versus those proposed by clients like TIBCO or Informatica.
Rok Dolinsek - PeerSpot reviewer
Manager, Bussines Development & Co Owner at Troia d.o.o.
Enables on-premise implementation with powerful data processing capabilities
This is the only solution that is possible to install on-premise. Cloudera provides a hybrid solution that combines compute on cloud or on-premises. It includes all machine learning algorithms in the Spark machine learning library. All functionalities needed for a big data platform and ETL are on the platform, eliminating the need for other tools. It is scalable, ready for vertical scaling, and very powerful, offering numerous functionalities and configurations for generative AI.
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Top Industries

By visitors reading reviews
Financial Services Firm
22%
Computer Software Company
7%
Healthcare Company
7%
Manufacturing Company
7%
Financial Services Firm
25%
Computer Software Company
8%
Manufacturing Company
7%
University
6%
Financial Services Firm
21%
Marketing Services Firm
9%
Computer Software Company
8%
Comms Service Provider
6%
 

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 Business28
Midsize Enterprise15
Large Enterprise32
By reviewers
Company SizeCount
Small Business16
Midsize Enterprise9
Large Enterprise31
 

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 a...
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...
What do you like most about Apache Spark?
We use Spark to process data from different data sources.
What is your experience regarding pricing and costs for Apache Spark?
Apache Spark is open-source, so it doesn't incur any charges.
What needs improvement with Apache Spark?
Areas for improvement are obviously ease of use considerations, though there are limitations in doing that, so while ...
What do you like most about Cloudera Distribution for Hadoop?
The tool can be deployed using different container technologies, which makes it very scalable.
What is your experience regarding pricing and costs for Cloudera Distribution for Hadoop?
The price for Cloudera is average, yet it is very good compared to other solutions. It can be deployed on-premises, u...
What needs improvement with Cloudera Distribution for Hadoop?
If they could support modifying the data more easily than the current implementation, it would be beneficial.
 

Also Known As

Amazon Elastic MapReduce
No data available
No data available
 

Overview

 

Sample Customers

Yelp
NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions
37signals, Adconion,adgooroo, Aggregate Knowledge, AMD, Apollo Group, Blackberry, Box, BT, CSC
Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop. Updated: January 2026.
881,757 professionals have used our research since 2012.