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 9.9% mindshare in H, compared to Apache’s 5.1% 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

3,175 Views
1,270 Comparison Views

83% willing to recommend

Spark SQL

Read 15 Spark SQL reviews

1,242 Views
1,105 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: June 2026).

Buyer's Guide

Amazon EMR vs. Spark SQL

June 2026

Download the complete report

Helped 899,052 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 June 2026, in the Hadoop category, the mindshare of Amazon EMR is 9.9%, down from 13.8% compared to the previous year. The mindshare of Spark SQL is 5.1%, down from 10.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop Mindshare Distribution
Product	Mindshare (%)
Amazon EMR	9.9%
Spark SQL	5.1%
Other	85.0%

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

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.

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

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

"Amazon makes it about as easy as it can be to dip a toe in those waters."

"Overall a good cloud infrastructure and takes the burden of managing the distributed hardware and can easily scale up and scale down as per the requirement of the task at hand."

"The project management is very streamlined."

"The initial setup is straightforward."

"A highly scalable platform to run vast amounts of data processing with very high efficiency."

"It's made life very easy."

"It's helped to automate processes."

More Amazon EMR pros

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

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

"I find the Thrift connection valuable."

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

"The scalability of the solution is good."

"We use it to gather all the transaction data."

"Overall the solution is excellent."

"Spark SQL gives us a handful of methods to design queries based on its own syntax and also incorporates the regular SQL syntax within tasks."

More Spark SQL pros

Cons

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

"Spark jobs take longer on Amazon EMR compared to previous experiences."

"There is room for improvement in pricing."

"The problem for us is it starts very slow; they need to improve the start time."

"The initial setup was time-consuming, and deployment took approximately 30 minutes."

"The initial setup was time-consuming."

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

"Better monitoring, debugging, and stability are all needed."

More Amazon EMR cons

"In the next release, maybe the visualization of some command-line features could be added."

"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."

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

"Anything to improve the GUI would be helpful."

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

"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."

"The initial setup is a bit complex."

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

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."

"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'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."

"Amazon EMR is not very expensive."

"The price of the solution is expensive."

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

More Amazon EMR pricing and cost advice

"The solution is open-sourced and free."

"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 bundled with Palantir Foundry at no extra charge."

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

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

"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%

Manufacturing Company

Construction Company

Healthcare Company

Financial Services Firm

20%

University

11%

Retailer

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	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

Comparisons

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Compared 7% of the time

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Compared 6% of the time

Apache Spark vs Amazon EMR

Compared 6% of the time

IBM Netezza Performance Server vs Amazon EMR

Compared 4% of the time

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Compared 20% of the time

IBM Db2 Big SQL vs Spark SQL

Compared 19% of the time

HPE Data Fabric vs Spark SQL

Compared 11% of the time

IBM Netezza Performance Server vs Spark SQL

Compared 9% of the time

IBM Analytics Engine vs Spark SQL

Compared 8% of the time

More Spark SQL Competitors

Product Reports

Buyer's Guide

Amazon EMR

June 2026

Download Amazon EMR product report

Buyer's Guide

Spark SQL

May 2026

Download Spark SQL product report

Also Known As

Amazon Elastic MapReduce

No data available

Overview

Amazon EMR simplifies big data processing by offering integration with popular tools. It's scalable and cost-efficient, enabling fast processing while managing infrastructure effortlessly. It's designed for users aiming to streamline data workflows and leverage its batch processing capabilities effectively.

Amazon EMR is a managed service that provides robust features for big data processing. It integrates seamlessly with S3, EC2, Hive, and Spark to facilitate sophisticated data transformation tasks and infrastructure management. It allows organizations to run data lakes, Spark, and Hadoop clusters effortlessly, offering flexibility with on-demand execution and extensive scalability. The platform is valued for its strong processing speed and comprehensive security features, making it ideal for complex data engineering projects. It supports both batch processing and real-time workflows, designed to eliminate hardware management while maintaining cost efficiency and stability.

What are the key features of Amazon EMR?

Cluster Management: Offers intuitive control and configuration of clusters
Integration: Seamlessly integrates with S3, EC2, Spark, and more
Scalability: Provides flexible scaling to meet data demands
Batch Processing: Allows efficient handling of large data sets
Cost Efficiency: Minimizes costs with managed service offerings

What benefits and ROI should be considered?

Processing Speed: Fast performance for data processing tasks
Security: Built-in features ensure data protection
Infrastructure Simplification: Eliminates hardware management needs
Flexibility: Adapts to changing data loads with ease
Affordability: Offers economic processing power

Amazon EMR is implemented by industries such as healthcare and tech processing for complex data tasks like building data lakes or financial data processing. It supports AI-driven analytics and data engineering projects, integrating with SageMaker for predictions and maintaining workflows in public health applications, allowing professionals in different fields to manage data pipelines, resource utilization, and job execution efficiently.

Amazon Web Services (AWS)

Spark SQL leverages SQL capabilities to process large datasets, offering high performance, seamless integration with Spark programs, and the ability to run parallel queries. It supports Hive interoperability and facilitates data transformation with DataFrames and Datasets.

Spark SQL enables efficient data engineering, transformation, and analytics for organizations dealing with large-scale data processing. It supports big data queries, builds data pipelines and warehouses, and interfaces with various databases, especially in distributed settings such as Hadoop and Azure. Users employ Spark SQL to establish business logic in Jupyter notebooks and facilitate data loading into SQL Server, enabling analytics with tools like Power BI. The documentation and flexibility to manage extensive data processing are valued by users, although a steep learning curve and documentation clarity are noted challenges. Enhancements for data visualization, GUI, and resource management alongside better integration with tools like Tableau are recommended.

What are the key features of Spark SQL?

Query Language: Supports complex SQL queries for effective large dataset processing.
Seamless Integration: Easily integrates into Spark programs, boosting performance and speed.
Parallel Queries: Capable of running numerous queries in parallel to enhance processing efficiency.
Interoperability with Hive: Facilitates interaction within distributed data ecosystems efficiently.
Data Transformation: Utilizes DataFrames and Datasets for flexible data manipulation.

What benefits or ROI should users consider in reviews?

Performance: Offers high-speed data processing for demanding workloads.
Scalability: Efficiently handles increasing data volumes and processing demands.
Ease of Use: Facilitates simpler SQL-based data management operations.
Flexibility: Adapts to various data sources and formats for broad usability.

In industries, Spark SQL is a critical part of data engineering, transformation, and analytics. It empowers organizations to manage big data processing and analytics in sectors like finance, healthcare, and telecommunications. By enabling seamless data pipeline creation, it supports real-time business decision-making processes and data-driven strategies across sectors.

Apache

Sample Customers

Yelp

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

Buyer's Guide

Amazon EMR vs. Spark SQL

June 2026

Free Report: Amazon EMR vs. Spark SQL

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