Amazon EMR vs Apache Spark comparison

Amazon Web Services (AWS) and Apache are both solutions in the Hadoop category. Amazon Web Services (AWS) is ranked #3 with an average rating of 7.8, while Apache is ranked #1 with an average rating of 8.7. Amazon Web Services (AWS) holds a 10.8% mindshare in H, compared to Apache’s 13.9% mindshare. Additionally, 83% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 90% of Apache users who would recommend it.

Amazon EMR

Read 25 Amazon EMR reviews

1,599 Views
560 Comparison Views

83% willing to recommend

Apache Spark

Read 68 Apache Spark reviews

3,530 Views
812 Comparison Views

90% willing to recommend

Amazon EMR

Apache Spark

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Amazon EMR and Apache Spark based on real PeerSpot user reviews.

Find out in this report how the two Hadoop solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Amazon EMR vs. Apache Spark Report (Updated: December 2025).

Buyer's Guide

Amazon EMR vs. Apache Spark

December 2025

Download the complete report

Helped 879,422 peers since 2012

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.

No quotes available

For more quotes and insights, download the Amazon EMR report

No quotes available

For more quotes and insights, download the Apache Spark report

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.

We get all call support, screen sharing support, and immediate support, so there are no problems.

reviewer1343079

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.

reviewer2043696

Senior Technical Engineer at a transportation company with 5,001-10,000 employees

They help with billing, cost determination, IAM properties, security compliance, and deployment and migration activities.

Mirza Mujtaba Baig

Lead AWS Data Engineer at Fission Labs

For more quotes and insights, download the Amazon EMR report

I have received support via newsgroups or guidance on specific discussions, which is what I would expect in an open-source situation.

Devindra Weerasooriya

Data Architect at Devtech

For more quotes and insights, download the Apache Spark report

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.

Scalability can be provisioned using the auto-scaling feature, EC2 instances, on-demand instances, and storage locations like block storage, S3, or file storage.

Mirza Mujtaba Baig

Lead AWS Data Engineer at Fission Labs

For more quotes and insights, download the Amazon EMR report

No quotes available

For more quotes and insights, download the Apache Spark report

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.

Regular updates, patch installations, monitoring, logging, alerting, and disaster recovery activities are crucial for maintaining stability.

Mirza Mujtaba Baig

Lead AWS Data Engineer at Fission Labs

For more quotes and insights, download the Amazon EMR report

Apache Spark resolves many problems in the MapReduce solution and Hadoop, such as the inability to run effective Python or machine learning algorithms.

Omar Khaled

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.

Devindra Weerasooriya

Data Architect at Devtech

For more quotes and insights, download the Apache Spark report

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.

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.

reviewer1343079

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.

Mirza Mujtaba Baig

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.

reviewer2043696

Senior Technical Engineer at a transportation company with 5,001-10,000 employees

For more quotes and insights, download the Amazon EMR report

Various tools like Informatica, TIBCO, or Talend offer specific aspects, licensing can be costly;

Devindra Weerasooriya

Data Architect at Devtech

For more quotes and insights, download the Apache Spark report

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.

Cost optimization can be achieved through instance usage, cluster sharing, and auto-scaling.

Mirza Mujtaba Baig

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.

reviewer2043696

Senior Technical Engineer at a transportation company with 5,001-10,000 employees

For more quotes and insights, download the Amazon EMR report

No quotes available

For more quotes and insights, download the Apache Spark report

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.

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.

Mirza Mujtaba Baig

Lead AWS Data Engineer at Fission Labs

Amazon EMR provides out-of-the-box solutions with Spark and Hive.

reviewer1343079

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.

reviewer2043696

Senior Technical Engineer at a transportation company with 5,001-10,000 employees

For more quotes and insights, download the Amazon EMR report

Not all solutions can make this data fast enough to be used, except for solutions such as Apache Spark Structured Streaming.

Omar Khaled

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.

Devindra Weerasooriya

Data Architect at Devtech

For more quotes and insights, download the Apache Spark report

Categories and Ranking

Amazon EMR

Ranking in Hadoop

3rd

Average Rating

7.8

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (13th)

Apache Spark

Ranking in Hadoop

1st

Average Rating

8.4

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

Compute Service (5th), Java Frameworks (2nd)

Mindshare comparison

As of January 2026, in the Hadoop category, the mindshare of Amazon EMR is 10.8%, down from 14.2% compared to the previous year. The mindshare of Apache Spark is 13.9%, down from 18.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop Market Share Distribution
Product	Market Share (%)
Apache Spark	13.9%
Amazon EMR	10.8%
Other	75.3%

Hadoop

Featured Reviews

reviewer1343079

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.

Read full review

Devindra Weerasooriya

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.

Read full review

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Top Industries

By visitors reading reviews

Financial Services Firm

22%

Educational Organization

13%

Computer Software Company

Healthcare Company

Financial Services Firm

25%

Computer Software Company

11%

Manufacturing Company

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	6
Midsize Enterprise	5
Large Enterprise	12

By reviewers
Company Size	Count
Small Business	28
Midsize Enterprise	15
Large Enterprise	32

Questions from the Community

What is your experience regarding pricing and costs for Amazon EMR?

Compared to others, Amazon seems efficient and is considered good for Big Data workloads. Costs are involved based on cluster resources, data volumes, EC2 ( /products/amazon-ec2-reviews ) instances...

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What needs improvement with Amazon EMR?

See all answers

What advice do you have for others considering Amazon EMR?

I am working on Amazon EMR but not extensively. Basically, our work is data transformation. Our pipelines work on that exclusively. We have Spark applications, and earlier, we used Amazon EMR exten...

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What do you like most about Apache Spark?

We use Spark to process data from different data sources.

See all answers

What is your experience regarding pricing and costs for Apache Spark?

Apache Spark is open-source, so it doesn't incur any charges.

See all answers

What needs improvement with Apache Spark?

Areas for improvement are obviously ease of use considerations, though there are limitations in doing that, so while various tools like Informatica, TIBCO, or Talend offer specific aspects, licensi...

See all answers

Comparisons

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Compared 28% of the time

Cloudera Distribution for Hadoop vs Amazon EMR

Compared 15% of the time

Amazon Redshift vs Amazon EMR

Compared 9% of the time

Cloudera Data Platform vs Amazon EMR

Compared 8% of the time

HPE Data Fabric vs Amazon EMR

Compared 7% of the time

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Spring Boot vs Apache Spark

Compared 18% of the time

AWS Lambda vs Apache Spark

Compared 8% of the time

AWS Batch vs Apache Spark

Compared 7% of the time

SAP HANA vs Apache Spark

Compared 6% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 4% of the time

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Product Reports

Buyer's Guide

Amazon EMR

December 2025

Download Amazon EMR product report

Buyer's Guide

Apache Spark

December 2025

Download Apache Spark product report

Also Known As

Amazon Elastic MapReduce

No data available

Overview

Amazon Elastic MapReduce (Amazon EMR) is a web service that makes it easy to quickly and cost-effectively process vast amounts of data. Amazon EMR simplifies big data processing, providing a managed Hadoop framework that makes it easy, fast, and cost-effective for you to distribute and process vast amounts of your data across dynamically scalable Amazon EC2 instances.

Amazon Web Services (AWS)

Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory

Apache

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

Buyer's Guide

Amazon EMR vs. Apache Spark

December 2025

Free Report: Amazon EMR vs. Apache Spark

Find out what your peers are saying about Amazon EMR vs. Apache Spark and other solutions. Updated: December 2025.

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