Amazon EMR vs Spark SQL 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 #5. Amazon Web Services (AWS) holds a 10.8% mindshare in H, compared to Apache’s 6.6% mindshare. Additionally, 83% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 85% of Apache users who would recommend it.

Amazon EMR

Read 25 Amazon EMR reviews

1,599 Views
560 Comparison Views

83% willing to recommend

Spark SQL

Read 14 Spark SQL reviews

637 Views
554 Comparison Views

85% willing to recommend

Amazon EMR

Spark SQL

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Amazon EMR and Spark SQL 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. Spark SQL Report (Updated: December 2025).

Buyer's Guide

Amazon EMR vs. Spark SQL

December 2025

Download the complete report

Helped 879,927 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:

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)

Spark SQL

Ranking in Hadoop

5th

Average Rating

7.8

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

No ranking in other categories

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 Spark SQL is 6.6%, down from 10.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop Market Share Distribution
Product	Market Share (%)
Amazon EMR	10.8%
Spark SQL	6.6%
Other	82.6%

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

Sahil Taneja

Principal Consultant/Manager at Tenzing

Easy to use and do not require a learning curve

Spark SQL can improve the documentation they have provided. It can be a bit unclear at times. They could improve the documentation a bit more so that we can understand it more easily. Moreover, they could improve SparkUI to have more advanced versions of the performance and the queries and all.

Read full review

Quotes from Members

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

Pros

"The ability to resize the cluster is what really makes it stand out over other Hadoop and big data solutions."

"In Amazon EMR it is easy to rebuild anything, easy to upgrade and has good fault tolerance."

"The solution helps us manage huge volumes of data."

"The initial setup is pretty straightforward."

"I rate Amazon EMR as ten out of ten."

"It has a variety of options and support systems."

"The solution is scalable."

"We are using applications, such as Splunk, Livy, Hadoop, and Spark. We are using all of these applications in Amazon EMR and they're helping us a lot."

More Amazon EMR pros

"Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline."

"The team members don't have to learn a new language and can implement complex tasks very easily using only SQL."

"The speed of getting data."

"Certain data sets that are very large are very difficult to process with Pandas and Python libraries. Spark SQL has helped us a lot with that."

"The performance is one of the most important features. It has an API to process the data in a functional manner."

"I find the Thrift connection valuable."

"One of Spark SQL's most beautiful features is running parallel queries to go through enormous data."

"The stability was fine. It behaved as expected."

More Spark SQL pros

Cons

"The product must add some of the latest technologies to provide more flexibility to the users."

"Modules and strategies should be better handled and notified early in advance."

"The legacy versions of the solution are not supported in the new versions."

"There were times where they would release new versions and it seemed to end up breaking old versions, which is very strange."

"I feel some lack of functionality in Amazon EMR."

"Amazon EMR can improve by adding some features, such as megastore services and HiveServer2. Additionally, the user interface could be better, similar to what Apache service provides, cross-platform services."

"There is no need to pay extra for third-party software."

"The most complicated thing is configuring to the cluster and ensure it's running correctly."

More Amazon EMR cons

"In the next update, we'd like to see better performance for small points of data. It is possible but there are better tools that are faster and cheaper."

"There should be better integration with other solutions."

"In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL."

"I've experienced some incompatibilities when using the Delta Lake format."

"It would be beneficial for aggregate functions to include a code block or toolbox that explains its calculations or supported conditional statements."

"It would be useful if Spark SQL integrated with some data visualization tools."

"There are many inconsistencies in syntax for the different querying tasks."

"The solution needs to include graphing capabilities. Including financial charts would help improve everything overall."

More Spark SQL cons

Pricing and Cost Advice

"There is no need to pay extra for third-party software."

"The product is not cheap, but it is not expensive."

"Amazon EMR's price is reasonable."

"You don't need to pay for licensing on a yearly or monthly basis, you only pay for what you use, in terms of underlying instances."

"There is a small fee for the EMR system, but major cost components are the underlying infrastructure resources which we actually use."

"The price of the solution is expensive."

"Amazon EMR is not very expensive."

"I rate the tool's pricing a five out of ten. It can be expensive since it's a managed service, and if you are not careful, you can run into unexpected charges. You can make a mistake that costs you tens of thousands of dollars. That's happened to us twice, so I'm sensitive to it. We're still trying to work on that. Our smallest client probably spends a hundred thousand dollars yearly on licensing, while our largest is well over a million."

More Amazon EMR pricing and cost advice

"We don't have to pay for licenses with this solution because we are working in a small market, and we rely on open-source because the budgets of projects are very small."

"We use the open-source version, so we do not have direct support from Apache."

"The on-premise solution is quite expensive in terms of hardware, setting up the cluster, memory, hardware and resources. It depends on the use case, but in our case with a shared cluster which is quite large, it is quite expensive."

"The solution is bundled with Palantir Foundry at no extra charge."

"There is no license or subscription for this solution."

"The solution is open-sourced and free."

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Top Industries

By visitors reading reviews

Financial Services Firm

23%

Educational Organization

13%

Computer Software Company

Healthcare Company

Financial Services Firm

15%

University

15%

Retailer

12%

Manufacturing Company

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	5
Midsize Enterprise	5
Large Enterprise	4

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.

See all answers

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.

See all answers

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.

See all answers

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Comparisons

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Compared 13% of the time

Amazon Redshift vs Amazon EMR

Compared 9% of the time

Apache Spark vs Amazon EMR

Compared 9% of the time

Oracle Exadata vs Amazon EMR

Compared 3% of the time

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Compared 29% of the time

HPE Data Fabric vs Spark SQL

Compared 16% of the time

SAP HANA vs Spark SQL

Compared 15% of the time

IBM Db2 Big SQL vs Spark SQL

Compared 15% of the time

IBM Netezza Performance Server vs Spark SQL

Compared 13% of the time

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Product Reports

Buyer's Guide

Amazon EMR

January 2026

Download Amazon EMR product report

Buyer's Guide

Spark SQL

December 2025

Download Spark SQL 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 SQL is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. There are several ways to interact with Spark SQL including SQL and the Dataset API. When computing a result the same execution engine is used, independent of which API/language you are using to express the computation. This unification means that developers can easily switch back and forth between different APIs based on which provides the most natural way to express a given transformation.

Apache

Sample Customers

Yelp

UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, Hitachi Solutions

Buyer's Guide

Amazon EMR vs. Spark SQL

December 2025

Free Report: Amazon EMR vs. Spark SQL

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

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