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 with an average rating of 8.0. Amazon Web Services (AWS) holds a 10.4% mindshare in H, compared to Apache’s 6.1% mindshare. Additionally, 83% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 86% of Apache users who would recommend it.

Amazon EMR

Read 25 Amazon EMR reviews

1,929 Views
656 Comparison Views

83% willing to recommend

Spark SQL

Read 15 Spark SQL reviews

752 Views
662 Comparison Views

86% 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: March 2026).

Buyer's Guide

Amazon EMR vs. Spark SQL

March 2026

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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 (14th)

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 April 2026, in the Hadoop category, the mindshare of Amazon EMR is 10.4%, down from 13.6% compared to the previous year. The mindshare of Spark SQL is 6.1%, down from 10.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop Mindshare Distribution
Product	Mindshare (%)
Amazon EMR	10.4%
Spark SQL	6.1%
Other	83.5%

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.

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Kemal Duman

Team Lead, Data Engineering at Nesine.com

Data pipelines have run faster and support flexible batch and streaming transformations

We do not have any performance problems, but we do have some resource problems. Spark SQL consumes so many resources that we migrated our streaming job from Spark to Apache Flink. Resource management in Spark SQL should be better. It consumes more resources, which is normal. The main reason we switched from Spark is memory and CPU consumption. The major reason is the resource problem because the number of streaming jobs has been increasing in our company. That is why we considered resource management as a priority. Because of the resource consumption, I would say the development of Spark SQL is better. For development purposes, it is a top product and not difficult to work with, but resources are the major problem. We changed to Flink regardless of development time. Development time is less in Spark compared with Flink.

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Quotes from Members

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

Pros

"The solution is scalable."

"The stability of the product has been great overall."

"The solution is scalable."

"Amazon EMR has multiple connectors that can connect to various data sources."

"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."

"This tool is simple to use and it's really accessible to many dev teams."

"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."

"Less expensive than in-house hosting, much more scalable when needed to process larger volumes of data."

More Amazon EMR pros

"It is a stable solution."

"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."

"This solution is a much more scalable and adventurous solution."

"Speed is the major benefit of using Spark SQL."

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

"The speed of getting data, as our TBs are big and it's a lot of data."

"Data validation and ease of use are the most valuable features."

"Offers a variety of methods to design queries and incorporates the regular SQL syntax within tasks."

More Spark SQL pros

Cons

"In general, I would rate the solution at a four out of ten."

"The solution can become expensive if you are not careful."

"The cost of Amazon EMR is very high."

"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."

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

"The product's features for storing data in static clusters could be better."

"The web interface for managing all your cloud services is a bit patchy and needs improvement."

"Amazon EMR is continuously improving, but maybe something like CI/CD out-of-the-box or integration with Prometheus Grafana."

More Amazon EMR cons

"SparkUI could have more advanced versions of the performance and the queries and all."

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

"Anything to improve the GUI would be helpful."

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

"This solution could be improved by adding monitoring and integration for the EMR."

"Being a new user, I am not able to find out how to partition it correctly."

"There should be better integration with other solutions."

"There are many inconsistencies in syntax for the different querying tasks like selecting columns and joining between two tables so I'd like to see a more consistent syntax."

More Spark SQL cons

Pricing and Cost Advice

"The cost of Amazon EMR is very high."

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

"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."

"The price of the solution is 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."

"Amazon EMR is not very expensive."

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."

"The solution is open-sourced and free."

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

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

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

19%

Healthcare Company

Manufacturing Company

Performing Arts

Financial Services Firm

16%

University

13%

Retailer

11%

Healthcare 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	6
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

What needs improvement with Spark SQL?

See all answers

What is your primary use case for Spark SQL?

Spark SQL has been in our stack for less than one year, though some of our colleagues are using it. It is a useful product for transformation jobs. We generally use Spark SQL for batch processing. ...

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What advice do you have for others considering Spark SQL?

Regarding the Catalyst query optimizer, I think we are using it. We were using it in the past, but I am not certain if we use it now. We used it a long time ago. I rate my experience with Spark SQL...

See all answers

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Product Reports

Buyer's Guide

Amazon EMR

March 2026

Download Amazon EMR product report

Buyer's Guide

Spark SQL

March 2026

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

March 2026

Free Report: Amazon EMR vs. Spark SQL

Find out what your peers are saying about Amazon EMR vs. Spark SQL and other solutions. Updated: March 2026.

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