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 8.5, while Apache is ranked #5 with an average rating of 7.0. Amazon Web Services (AWS) holds a 13.0% mindshare in H, compared to Apache’s 9.8% mindshare. Additionally, 86% 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 23 Amazon EMR reviews

1,760 Views
616 Comparison Views

86% willing to recommend

Spark SQL

Read 14 Spark SQL reviews

639 Views
562 Comparison Views

85% willing to recommend

Amazon EMR

Spark SQL

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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: September 2025).

Buyer's Guide

Amazon EMR vs. Spark SQL

September 2025

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

7.2

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 September 2025, in the Hadoop category, the mindshare of Amazon EMR is 13.0%, down from 15.1% compared to the previous year. The mindshare of Spark SQL is 9.8%, down from 10.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop Market Share Distribution
Product	Market Share (%)
Amazon EMR	13.0%
Spark SQL	9.8%
Other	77.2%

Hadoop

Featured Reviews

Prashant Singh

Vice President -Product Management at Paytm

Seamless data integration enhances reporting efficiency and an easy setup

Amazon EMR has multiple connectors that can connect to various data sources. The service charges are based on processing only, depending on the resources used, which can help save money. It is easy to integrate with other services for storage, allowing data to be shifted to cheaper storage based on usage.

Read full review

SurjitChoudhury

Data engineer at Cocos pt

Offers the flexibility to handle large-scale data processing

My experience with the initial setup of Spark SQL was relatively smooth. Understanding the system wasn't overly difficult because the data was structured in databases, and we could use notebooks for coding in Python or Java. Configuring networks and running scripts to load data into the database were routine tasks that didn't pose significant challenges. The flexibility to use different languages for coding and the ability to process data using key-value pairs in Python made the setup adaptable. Once we received the source data, processing it in SparkSQL involved writing scripts to create dimension and fact tables, which became a standard part of our workflow. Setting up Spark SQL was reasonably quick, but sometimes we face performance issues, especially during data loading into the SQL Server data warehouse. Sequencing notebooks for efficient job runs is crucial, and managing complex tasks with multiple notebooks requires careful tracking. Exploring ways to optimize this process could be beneficial. However, once you are familiar with the database architecture and project tools, understanding and adapting to the system become more straightforward.

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 security of the managed workflow and the managed services are the best features for us. Since we inherited their security model and it's all managed services, those are the key benefits for our clients."

"The solution is pretty simple to set up."

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

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

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

"It allows users to access the data through a web interface."

"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."

"When we grade big jobs from on-prem to the cloud, we do it in EMR with Spark."

More Amazon EMR pros

"It is a stable solution."

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

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

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

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

"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 speed of getting data."

More Spark SQL pros

Cons

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

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

"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."

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

"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."

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

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

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

More Amazon EMR cons

"There should be better integration with other solutions."

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

"Anything to improve the GUI would be helpful."

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

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

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

"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. 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."

More Spark SQL cons

Pricing and Cost Advice

"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."

"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."

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

"Amazon EMR is not very expensive."

"Amazon EMR's price is reasonable."

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."

"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."

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

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Top Industries

By visitors reading reviews

Financial Services Firm

24%

Computer Software Company

13%

Educational Organization

11%

Manufacturing Company

Financial Services Firm

18%

University

11%

Retailer

10%

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	10

By reviewers
Company Size	Count
Small Business	5
Midsize Enterprise	5
Large Enterprise	4

Questions from the Community

What do you like most about Amazon EMR?

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

See all answers

What is your experience regarding pricing and costs for Amazon EMR?

Compared to others, Amazon seems efficient and is considered good for Big Data workloads. Costs are involved based on cluster resources, data volumes, EC2 ( /products/amazon-ec2-reviews ) instances...

See all answers

What needs improvement with Amazon EMR?

There is room for improvement with respect to retries, handling the volume of data on S3 ( /products/amazon-s3-reviews ) buckets, cluster provisioning, scaling, termination, security, and integrati...

See all answers

What do you like most about Spark SQL?

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

See all answers

What needs improvement with Spark SQL?

In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL. There could be additional features that I haven't explored but the current solution for working ...

See all answers

What is your primary use case for Spark SQL?

I employ Spark SQL for various tasks. Initially, I gathered data from databases, SAP systems, and external sources via SFTP, storing it in blob storage. Using Spark SQL within Jupyter notebooks, I ...

See all answers

Comparisons

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Compared 10% of the time

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Compared 8% of the time

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Compared 54% of the time

SAP HANA vs Spark SQL

Compared 20% of the time

IBM Db2 Big SQL vs Spark SQL

Compared 14% of the time

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Product Reports

Buyer's Guide

Amazon EMR

September 2025

Download Amazon EMR product report

Buyer's Guide

Spark SQL

August 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

September 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: September 2025.

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