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

Download the report

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: April 2026).

Buyer's Guide

Amazon EMR vs. Spark SQL

April 2026

Download the complete report

Helped 896,563 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

"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."

"The initial setup is pretty straightforward."

"Amazon EMR is a good solution that can be used to manage big data."

"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 stability of the product has been great overall."

"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."

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

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

More Amazon EMR pros

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

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

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

"I find the Thrift connection valuable."

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

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

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

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

More Spark SQL pros

Cons

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

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

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

"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."

"We don't have much control. If we have multiple users, if they want to scale up, the cost will go and increase and we don't know how we can restrict that price part."

"One of the lacking features is good web support."

"Whenever we have scaling policies for load balancing and our load increases the input rate, we increase the resources. However, at that time Amazon AWS is not providing the scale of the required resources in a short time."

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

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."

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

"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."

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

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

"Spark SQL consumes so many resources that we migrated our streaming job from Spark to Apache Flink."

"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."

"Amazon EMR's price is reasonable."

"Amazon EMR is not very expensive."

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

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

"The cost of Amazon EMR is very high."

"The price of the solution is expensive."

More Amazon EMR pricing and cost advice

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

"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."

"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 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."

See which vendors are best for you

Use our free recommendation engine to learn which Hadoop solutions are best for your needs.

See recommendations

896,563 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

20%

Healthcare Company

Manufacturing Company

Comms Service Provider

Financial Services Firm

20%

University

12%

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. ...

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

Snowflake vs Amazon EMR

Compared 9% of the time

Amazon Redshift vs Amazon EMR

Compared 7% of the time

Cloudera Distribution for Hadoop vs Amazon EMR

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

More Amazon EMR Competitors

Apache Spark vs Spark SQL

Compared 20% of the time

IBM Db2 Big SQL vs Spark SQL

Compared 20% of the time

HPE Data Fabric vs Spark SQL

Compared 12% of the time

IBM Netezza Performance Server vs Spark SQL

Compared 9% of the time

SAP HANA vs Spark SQL

Compared 7% of the time

More Spark SQL Competitors

Product Reports

Buyer's Guide

Amazon EMR

May 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

April 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: April 2026.

DOWNLOAD NOW

896,563 professionals have used our research since 2012.

See our Amazon EMR vs. Spark SQL report.

See our list of best Hadoop vendors.

We monitor all Hadoop reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.