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Amazon EMR vs Spark SQL comparison

 

Comparison Buyer's Guide

Executive Summary

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
24
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
14
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of October 2025, in the Hadoop category, the mindshare of Amazon EMR is 12.8%, down from 14.3% compared to the previous year. The mindshare of Spark SQL is 9.4%, down from 10.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop Market Share Distribution
ProductMarket Share (%)
Amazon EMR12.8%
Spark SQL9.4%
Other77.8%
Hadoop
 

Featured Reviews

Prashant  Singh - PeerSpot reviewer
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.
SurjitChoudhury - PeerSpot reviewer
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.

Quotes from Members

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

Pros

"The solution helps us manage huge volumes of data."
"It allows users to access the data through a web interface."
"Amazon EMR has multiple connectors that can connect to various data sources."
"The project management is very streamlined."
"I rate Amazon EMR as ten out of ten."
"In Amazon EMR it is easy to rebuild anything, easy to upgrade and has good fault tolerance."
"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."
"When we grade big jobs from on-prem to the cloud, we do it in EMR with Spark."
"This solution is useful to leverage within a distributed ecosystem."
"Data validation and ease of use are the most valuable features."
"The solution is easy to understand if you have basic knowledge of SQL commands."
"It is a stable solution."
"Overall the solution is excellent."
"I find the Thrift connection valuable."
"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."
 

Cons

"There is no need to pay extra for third-party software."
"There were times where they would release new versions and it seemed to end up breaking old versions, which is very strange."
"The initial setup was time-consuming."
"There is room for improvement in pricing."
"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."
"The dashboard management could be better. Right now, it's lacking a bit."
"As people are shifting from legacy solutions to other technologies, Amazon EMR needs to add more features that give more flexibility in managing user data."
"Modules and strategies should be better handled and notified early in advance."
"It would be useful if Spark SQL integrated with some data visualization tools."
"It takes a bit of time to get used to using this solution versus Pandas as it has a steep learning curve."
"SparkUI could have more advanced versions of the performance and the queries and all."
"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."
"The solution needs to include graphing capabilities. Including financial charts would help improve everything overall."
"I've experienced some incompatibilities when using the Delta Lake format."
"It would be beneficial for aggregate functions to include a code block or toolbox that explains its calculations or supported conditional statements."
"There should be better integration with other solutions."
 

Pricing and Cost Advice

"There is no need to pay extra for third-party software."
"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."
"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's price is reasonable."
"The price of the solution is expensive."
"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."
"The product is not cheap, but it is not expensive."
"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."
"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."
"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 solution is open-sourced and free."
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Top Industries

By visitors reading reviews
Financial Services Firm
23%
Computer Software Company
12%
Educational Organization
12%
Healthcare Company
7%
Financial Services Firm
17%
University
12%
Retailer
11%
Manufacturing Company
9%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business6
Midsize Enterprise5
Large Enterprise11
By reviewers
Company SizeCount
Small Business5
Midsize Enterprise5
Large Enterprise4
 

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.
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...
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...
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.
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 ...
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 ...
 

Comparisons

 

Also Known As

Amazon Elastic MapReduce
No data available
 

Overview

 

Sample Customers

Yelp
UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, Hitachi Solutions
Find out what your peers are saying about Amazon EMR vs. Spark SQL and other solutions. Updated: September 2025.
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