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

Download the complete report

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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 (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 March 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

"Amazon EMR provides out-of-the-box functionality because we can deploy and get Spark functionality over Hadoop."

"One of the valuable features about this solution is that it's managed services, so it's pretty stable, and scalable as much as you wish. It has all the necessary distributions. With some additional work, it's also possible to change to a Spark version with the latest version of EMR. It also has Hudi, so we are leveraging Apache Hudi on EMR for change data capture, so then it comes out-of-the-box in EMR."

"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."

"Amazon EMR's most valuable features are processing speed and data storage capacity."

"This is the best tool for hosts and it's really flexible and scalable."

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

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

"The solution is pretty simple to set up."

More Amazon EMR pros

"The solution is easy to understand if you have basic knowledge of SQL commands."

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

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

"This solution is useful to leverage within a distributed ecosystem."

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

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

"I find the Thrift connection valuable."

"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."

More Spark SQL pros

Cons

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

"The initial setup was time-consuming."

"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."

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

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

"The dashboard management could be better. Right now, it's lacking a bit."

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

More Amazon EMR cons

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

"Being a new user, I am not able to find out how to partition it correctly. I probably need more information or knowledge. In other database solutions, you can easily optimize all partitions. I haven't found a quicker way to do that in Spark SQL. It would be good if you don't need a partition here, and the system automatically partitions in the best way. They can also provide more educational resources for new users."

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

"Anything to improve the GUI would be helpful."

"It takes a bit of time to get used to using this solution versus Pandas as it has a steep learning curve."

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

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

More Spark SQL cons

Pricing and Cost Advice

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

"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."

"The cost of Amazon EMR is very high."

"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."

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

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

"Amazon EMR is not very expensive."

"The price of the solution is 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 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."

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

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

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

"The solution is open-sourced and free."

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Top Industries

By visitors reading reviews

Financial Services Firm

20%

Computer Software Company

Healthcare Company

Manufacturing Company

Financial Services Firm

17%

University

13%

Retailer

12%

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. ...

See all answers

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

February 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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